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{{for|the journal|Machine Learning (journal)}}
{{Machine learning bar}}
{{See also|Pattern recognition}}


'''Machine learning''', a branch of [[artificial intelligence]], concerns the construction and study of systems that can [[learning|learn]] from data.
For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages.  After learning, it can then be used to classify new email messages into spam and non-spam folders.


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The core of machine learning deals with representation and generalization.  Representation of data instances and functions evaluated on these instances are part of all machine learning systems.  Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of [[computational learning theory]].
 
There are a wide variety of machine learning tasks and successful applications.
[[Optical character recognition]], in which [[wikt:hard copy|printed]] characters are recognized automatically based on previous examples, is a classic example of machine learning.<ref name=Wernick-Signal-Proc-July-2010>Wernick, Yang, Brankov, Yourganov and Strother, Machine Learning in Medical Imaging, ''[[IEEE Signal Processing Society|IEEE Signal Processing Magazine]]'', vol. 27, no. 4, July 2010, pp. 25-38</ref>
 
==Definition==
 
In 1959, [[Arthur Samuel]] defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".<ref name="arthur_samuel_machine_learning_def">{{cite book | title=Too Big to Ignore: The Business Case for Big Data | publisher=Wiley | author=Phil Simon | date=March 18, 2013 | pages=89 | isbn=978-1118638170 | url=http://books.google.gr/books?id=Dn-Gdoh66sgC&pg=PA89#v=onepage&q&f=false}}</ref>
 
[[Tom M. Mitchell]] provided a widely quoted, more formal definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E".<ref>* Mitchell, T. (1997). ''Machine Learning'', McGraw Hill. ISBN 0-07-042807-7, p.2.</ref>  This definition is notable for its defining machine learning in fundamentally [[Operational definition|operational]] rather than cognitive terms, thus following [[Alan Turing]]'s proposal in Turing's paper "[[Computing Machinery and Intelligence]]" that the question "Can machines think?" be replaced with the question "Can machines do what we (as thinking entities) can do?"<ref>{{Citation |chapterurl=http://eprints.ecs.soton.ac.uk/12954/ |first=Stevan |last=Harnad |year=2008 |chapter=The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence |editor1-last=Epstein |editor1-first=Robert |editor2-last=Peters |editor2-first=Grace |title=The Turing Test Sourcebook: Philosophical and Methodological Issues in the Quest for the Thinking Computer |location= |publisher=Kluwer |isbn= }}</ref>
 
==Generalization==
 
A core objective of a learner is to generalize from its experience.<ref>[[Christopher M. Bishop]] (2006) ''Pattern Recognition and Machine Learning'', Springer ISBN 0-387-31073-8.</ref><ref>[[Mehryar Mohri]], Afshin Rostamizadeh, Ameet Talwalkar (2012) ''Foundations of Machine Learning'', The
MIT Press ISBN 9780262018258.</ref> Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.
 
==Machine learning and data mining==
These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows:
* Machine learning focuses on prediction, based on ''known'' properties learned from the training data.
* [[Data mining]] focuses on the [[discovery (observation)|discovery]] of (previously) ''unknown'' properties in the data. This is the analysis step of [[Knowledge discovery|Knowledge Discovery]] in Databases.
 
The two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. On the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, [[ECML PKDD]] being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to ''reproduce known'' knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously ''unknown'' knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
 
==Human interaction==
 
Some machine learning systems attempt to eliminate the need for human [[Intuition (knowledge)|intuition]] in data analysis, while others adopt a collaborative approach between human and machine. Human intuition cannot, however, be entirely eliminated, since the system's designer must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data{{Citation needed|reason=I don't think there is general consensus on scientific community about this subject. "Human intuition cannot be [...] entirely eliminated" is a strong statement. If such citation cannot be provided, the paragraph should be removed.|date=September 2013}}.
 
==Algorithm types==<!--This section is linked from [[Learning]]-->
 
Machine learning [[algorithm]]s can be organized into a [[Taxonomy (general)|taxonomy]] based on the desired outcome of the algorithm or the type of input available during training the machine {{citation needed|date=March 2013}}.
* '''[[Supervised learning]] '''algorithms are trained on '''labelled''' examples, i.e., input where the desired output is known. The supervised learning algorithm attempts to generalise a function or mapping from inputs to outputs which can then be used to speculatively generate an output for previously unseen inputs.
* '''[[Unsupervised learning]]''' algorithms operate on '''unlabelled''' examples, i.e., input where the desired output is unknown. Here the objective is to discover structure in the data (e.g. through a [[cluster analysis]]), not to generalise a mapping from inputs to outputs.
* '''[[Semi-supervised learning]]''' combines both labeled and unlabelled examples to generate an appropriate function or classifier.
* '''[[Transduction (machine learning)|Transduction]]''', or ''transductive inference'',  tries to predict new outputs on specific and fixed (test) cases from observed, specific (training) cases.
* '''[[Reinforcement learning]]''' is concerned with how intelligent agents ought to act in an environment to maximise some notion of reward. The agent executes actions which cause the observable state of the environment to change. Through a sequence of actions, the agent attempts to gather knowledge about how the environment responds to its actions, and attempts to synthesise a sequence of actions that maximises a cumulative reward.
* '''[[Learning to learn]]''' learns its own [[inductive bias]] based on previous experience.
* [[Developmental robotics|Developmental learning]], elaborated for [[Robot learning]], generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers, and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.
 
==Theory==
{{Main|Computational learning theory}}
 
The computational analysis of machine learning algorithms and their performance is a branch of [[theoretical computer science]] known as [[computational learning theory]]. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common.
 
In addition to performance bounds, computational learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of [[time complexity]] results. Positive results show that a certain class of functions can be learned in [[polynomial time]]. Negative results show that certain classes cannot be learned in polynomial time.
 
There are many similarities between machine learning theory and [[statistical inference]], although they use different terms.
 
==Approaches==
 
{{Main|List of machine learning algorithms}}
 
===Decision tree learning===
{{Main|Decision tree learning}}
 
Decision tree learning uses a [[decision tree]] as a [[predictive modelling|predictive model]] which maps observations about an item to conclusions about the item's target value.
 
===Association rule learning===
{{Main|Association rule learning}}
 
Association rule learning is a method for discovering interesting relations between variables in large databases.
 
===Artificial neural networks===
{{Main|artificial neural network}}
 
An [[artificial neural network]] (ANN) learning algorithm, usually called "neural network" (NN), is a learning algorithm that is inspired by the structure and functional aspects of [[biological neural networks]]. Computations are structured in terms of an interconnected group of [[artificial neuron]]s, processing information using a [[connectionism|connectionist]] approach to [[computation]]. Modern neural networks are [[non-linear]] [[statistical]] [[data modeling]] tools. They are usually used to model complex relationships between inputs and outputs, to [[pattern recognition|find patterns]] in data, or to capture the statistical structure in an unknown [[joint probability distribution]] between observed variables.
 
===Inductive logic programming===
{{Main|Inductive logic programming}}
 
Inductive logic programming (ILP) is an approach to rule learning using [[logic programming]] as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program which [[Entailment|entails]] all the positive and none of the negative examples. [[Inductive programming]] is a related field that considers any kind of programming languages for representing hypotheses (and not only logic programming), such as functional programs.
 
===Support vector machines===
{{Main|Support vector machines}}
 
Support vector machines (SVMs) are a set of related [[supervised learning]] methods used for [[statistical classification|classification]] and [[regression analysis|regression]]. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.
 
===Clustering===
{{Main|Cluster analysis}}
Cluster analysis is the assignment of a set of observations into subsets (called ''clusters'') so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some ''similarity metric'' and evaluated for example by ''internal compactness'' (similarity between members of the same cluster) and ''separation'' between different clusters. Other methods are based on ''estimated density'' and ''graph connectivity''.
Clustering is a method of [[unsupervised learning]], and a common technique for [[statistics|statistical]] [[data analysis]].
 
===Bayesian networks===
{{Main|Bayesian network}}
 
A Bayesian network, belief network or directed acyclic graphical model is a [[graphical model|probabilistic graphical model]] that represents a set of [[random variables]] and their [[conditional independence|conditional independencies]] via a [[directed acyclic graph]] (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform [[inference]] and learning.
 
===Reinforcement learning===
{{Main|Reinforcement learning}}
 
Reinforcement learning is concerned with how an ''agent'' ought to take ''actions'' in an ''environment'' so as to maximize some notion of long-term ''reward''. Reinforcement learning algorithms attempt to find a ''policy'' that maps ''states'' of the world to the actions the agent ought to take in those states. Reinforcement learning differs from the [[supervised learning]] problem in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected.
 
===Representation learning===
 
Several learning algorithms, mostly [[unsupervised learning]] algorithms, aim at discovering better representations of the inputs provided during training. Classical examples include [[principal components analysis]] and [[cluster analysis]]. Representation learning algorithms often attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions, allowing to reconstruct the inputs coming from the unknown data generating distribution, while not being necessarily faithful for configurations that are implausible under that distribution. [[Manifold learning]] algorithms attempt to do so under the constraint that the learned representation is low-dimensional. [[Sparse coding]] algorithms attempt to do so under the constraint that the learned representation is sparse (has many zeros). [[Multilinear subspace learning]] algorithms aim to learn low-dimensional representations directly from [[tensor]] representations for multidimensional data, without reshaping them into (high-dimensional) vectors.<ref>{{cite journal
|first=Haiping |last=Lu
|first2=K.N. |last2=Plataniotis
|first3=A.N. |last3=Venetsanopoulos
|url=http://www.dsp.utoronto.ca/~haiping/Publication/SurveyMSL_PR2011.pdf
|title=A Survey of Multilinear Subspace Learning for Tensor Data
|journal=Pattern Recognition
|volume=44 |number=7 |pages=1540–1551 |year=2011
|doi=10.1016/j.patcog.2011.01.004
}}</ref> [[Deep learning]] algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.<ref>
{{cite book
| title = Learning Deep Architectures for AI
| author = Yoshua Bengio
| publisher = Now Publishers Inc.
| year = 2009
| isbn = 978-1-60198-294-0
| pages = 1–3
| url = http://books.google.com/books?id=cq5ewg7FniMC&pg=PA3
}}</ref>
 
===Similarity and metric learning===
{{Main|Similarity learning}}
 
In this problem, the learning machine is given pairs of examples that are considered similar and pairs of less similar objects. It then needs to learn a similarity function (or a distance metric function) that can predict if new objects are similar. It is sometimes used in [[Recommendation systems]].
 
===Sparse Dictionary Learning===
In this method, a datum is represented as a linear combination of basis functions, and the coefficients are assumed to be sparse. Let ''x'' be a ''d''-dimensional datum, ''D'' be a ''d'' by ''n'' matrix, where each column of ''D'' represents a basis function. ''r'' is the coefficient to represent ''x'' using ''D''. Mathematically, sparse dictionary learning means the following
<math>
x \approx D \times r
</math>
where ''r'' is sparse. Generally speaking, ''n'' is assumed to be larger than ''d'' to allow the freedom for a sparse representation.
 
Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine which classes a previously unseen datum belongs to. Suppose a dictionary for each class has already been built. Then a new datum is associated with the class such that it's best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image path can be sparsely represented by an image dictionary, but the noise cannot.<ref>Aharon, M, M Elad, and A Bruckstein. 2006. “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation.” Signal Processing, IEEE Transactions on 54 (11): 4311-4322</ref>
 
==Applications==
{{unreferenced section|date=February 2013}}
Applications for machine learning include:
 
{{refbegin|2}}
* [[Machine perception]]
* [[Computer vision]], including [[object recognition]]
* [[Natural language processing]]<ref>{{cite book |title=Speech and Language Processing |authors=Daniel Jurafsky and James H. Martin |publisher=Pearson Education |year=2009 |pages=207 ff.}}</ref>
* [[Syntactic pattern recognition]]
* [[Search engines]]
* [[Diagnosis (artificial intelligence)|Medical diagnosis]]
* [[Bioinformatics]]
* [[Brain-machine interfaces]]
* [[Cheminformatics]]
* Detecting [[credit card fraud]]
* [[Stock market]] analysis
* Classifying [[DNA sequence]]s
* [[Sequence mining]]
* [[Speech recognition|Speech]] and [[handwriting recognition]]
* [[Strategy game|Game playing]]<ref name='CACM'>{{cite journal|title=Temporal Difference Learning and [[TD-Gammon]] |journal=[[Communications of the ACM]] |date=March 1995|first=Gerald|last=Tesauro|coauthors=|volume=38|issue=3}}</ref>
* [[Software engineering]]
* [[Adaptive website]]s
* [[Robot locomotion]]
* [[Computational advertising]]
* [[Computational finance]]
* [[Structural health monitoring]]
* [[Sentiment analysis]] (or opinion mining)
* [[Affective computing]]
* [[Information retrieval]]
* [[Recommender system]]s
{{refend}}
 
In 2006, the online movie company [[Netflix]] held the first "[[Netflix Prize]]" competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%.  A joint team made up of researchers from [[AT&T Labs|AT&T Labs-Research]] in collaboration with the teams Big Chaos and Pragmatic Theory built an [[Ensemble Averaging|ensemble model]] to win the Grand Prize in 2009 for $1 million.<ref>[http://www2.research.att.com/~volinsky/netflix/ "BelKor Home Page"] research.att.com</ref>
 
In 2010 The Wall Street Journal wrote about a money management firm Rebellion Research's use of machine learning to predict economic movements, the article talks about Rebellion Research's prediction of the financial crisis and economic recovery.<ref>[http://online.wsj.com/news/articles/SB10001424052748703834604575365310813948080]</ref>
 
==Software==
 
[[Ayasdi]], [[Angoss|Angoss KnowledgeSTUDIO]], [[Apache Mahout]], [[Gesture Recognition Toolkit]], [[SPSS Modeler|IBM SPSS Modeler]], [[KNIME]], [[KXEN Inc.|KXEN Modeler]], [[LIONsolver]], [[MATLAB]], [[mlpy]], [[MLPACK (C++ library)|MLPACK library]], [[Monte Carlo Machine Learning Library|MCMLL]], [[OpenCV]], [[dlib]], [[Oracle Data Mining]], [[Orange (software)|Orange]], [[scikit-learn|Python scikit-learn]], [[R (programming language)|R]], [[RapidMiner]], Salford Predictive Modeler, [[SAS (software)#Components|SAS Enterprise Miner]], [[Shogun (toolbox)|Shogun toolbox]], [[STATISTICA|STATISTICA Data Miner]], and [[Weka (machine learning)|Weka]] are [[software suite]]s containing a variety of machine learning algorithms.
 
==Journals and conferences==
* [[Machine Learning (journal)|''Machine Learning'' (journal)]]
* ''[[Journal of Machine Learning Research]]''
* ''[[Neural Computation]]'' (journal)
* [http://www.degruyter.de/journals/jisys/detailEn.cfm Journal of Intelligent Systems(journal)]
* [[ICML|International Conference on Machine Learning (ICML)]] (conference)
* [[NIPS|Neural Information Processing Systems (NIPS)]] (conference)
 
==See also==
 
{{Portal|Artificial intelligence}}
 
{{refbegin|2}}
 
* [[Adaptive control]]
* [[Automatic reasoning]]
* [[Cache language model]]
* [[Computational intelligence]]
* [[Computational neuroscience]]
* [[Cognitive science]]
* [[Cognitive model]]ing
* [[Data mining]]
* [[Explanation-based learning]]
* [[Hidden Markov model]]
* [[List of machine learning algorithms]]
* [[List of important publications in computer science#Machine learning|Important publications in machine learning]]
* [[Multi-label classification]]
* [[Multilinear subspace learning]]
* [[Pattern recognition]]
* [[Predictive analytics]]
* [[Robot learning]]
* [[Developmental robotics]]
 
{{refend}}
 
==References==
 
{{Reflist|2}}
 
==Further reading==
 
{{Refbegin|2}}
* [[Mehryar Mohri]], Afshin Rostamizadeh, Ameet Talwalkar (2012). ''[http://www.cs.nyu.edu/~mohri/mlbook/ Foundations of Machine Learning]'', The MIT Press. ISBN 9780262018258.
* Ian H. Witten and Eibe Frank (2011). ''Data Mining: Practical machine learning tools and techniques'' Morgan Kaufmann, 664pp., ISBN 978-0123748560.
* Sergios Theodoridis, Konstantinos Koutroumbas (2009) "Pattern Recognition", 4th Edition, Academic Press, ISBN 978-1-59749-272-0.
* Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: ''YALE: Rapid Prototyping for Complex Data Mining Tasks'', in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.
* Bing Liu (2007), ''[http://www.cs.uic.edu/~liub/WebMiningBook.html Web Data Mining: Exploring Hyperlinks, Contents and Usage Data].'' Springer, ISBN 3-540-37881-2
* Toby Segaran (2007), ''Programming Collective Intelligence'', O'Reilly, ISBN 0-596-52932-5
* Huang T.-M., Kecman V., Kopriva I. (2006), [http://learning-from-data.com Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning],  Springer-Verlag, Berlin, Heidelberg, 260 pp.&nbsp;96 illus., Hardcover, ISBN 3-540-31681-7.
* Ethem Alpaydın (2004) ''Introduction to Machine Learning (Adaptive Computation and Machine Learning)'', MIT Press, ISBN 0-262-01211-1
* MacKay, D.J.C. (2003). ''[http://www.inference.phy.cam.ac.uk/mackay/itila/ Information Theory, Inference, and Learning Algorithms]'', Cambridge University Press.  ISBN 0-521-64298-1.
* KECMAN Vojislav (2001), [http://support-vector.ws Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models], The MIT Press, Cambridge, MA, 608 pp., 268 illus., ISBN 0-262-11255-8.
* [[Trevor Hastie]], [[Robert Tibshirani]] and Jerome Friedman (2001). ''[http://www-stat.stanford.edu/~tibs/ElemStatLearn/ The Elements of Statistical Learning]'', Springer. ISBN 0-387-95284-5.
* Richard O. Duda, Peter E. Hart, David G. Stork (2001) ''Pattern classification'' (2nd edition), Wiley, New York, ISBN 0-471-05669-3.
* Bishop, C.M. (1995). ''Neural Networks for Pattern Recognition'', Oxford University Press. ISBN 0-19-853864-2.
* Ryszard S. Michalski, George Tecuci (1994), ''Machine Learning: A Multistrategy Approach'', Volume IV, Morgan Kaufmann, ISBN 1-55860-251-8.
* Sholom Weiss and Casimir Kulikowski (1991). ''Computer Systems That Learn'', Morgan Kaufmann.  ISBN 1-55860-065-5.
* Yves Kodratoff, Ryszard S. Michalski (1990), ''Machine Learning: An Artificial Intelligence Approach, Volume III'', Morgan Kaufmann, ISBN 1-55860-119-8.
* Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1986), ''Machine Learning: An Artificial Intelligence Approach, Volume II'', Morgan Kaufmann, ISBN 0-934613-00-1.
* Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1983), ''Machine Learning: An Artificial Intelligence Approach'', Tioga Publishing Company, ISBN 0-935382-05-4.
* Vladimir Vapnik (1998). ''Statistical Learning Theory''. Wiley-Interscience, ISBN 0-471-03003-1.
* Ray Solomonoff, ''An Inductive Inference Machine'', IRE Convention Record, Section on Information Theory, Part 2, pp., 56-62, 1957.
* [[Ray Solomonoff]], "[http://world.std.com/~rjs/indinf56.pdf An Inductive Inference Machine]" A privately circulated report from the 1956 [[Dartmouth Conferences|Dartmouth Summer Research Conference on AI]].
{{Refend}}
 
==External links==
* [http://machinelearning.org/ International Machine Learning Society]
* Popular online course by [[Andrew Ng]], at [http://www.ml-class.org ml-class.org]. It uses [[GNU Octave]]. The course is a free version of [[Stanford University]]'s actual course taught by Ng, whose lectures are also [http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1 available for free].
* [http://videolectures.net/Top/Computer_Science/Machine_Learning/ Machine Learning Video Lectures]
 
[[Category:Learning in computer vision]]
[[Category:Machine learning| ]]
[[Category:Learning]]
[[Category:Cybernetics]]

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Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.

The core of machine learning deals with representation and generalization. Representation of data instances and functions evaluated on these instances are part of all machine learning systems. Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of computational learning theory.

There are a wide variety of machine learning tasks and successful applications. Optical character recognition, in which printed characters are recognized automatically based on previous examples, is a classic example of machine learning.[1]

Definition

In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".[2]

Tom M. Mitchell provided a widely quoted, more formal definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E".[3] This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in Turing's paper "Computing Machinery and Intelligence" that the question "Can machines think?" be replaced with the question "Can machines do what we (as thinking entities) can do?"[4]

Generalization

A core objective of a learner is to generalize from its experience.[5][6] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

Machine learning and data mining

These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows:

  • Machine learning focuses on prediction, based on known properties learned from the training data.
  • Data mining focuses on the discovery of (previously) unknown properties in the data. This is the analysis step of Knowledge Discovery in Databases.

The two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. On the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine. Human intuition cannot, however, be entirely eliminated, since the system's designer must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the dataPotter or Ceramic Artist Truman Bedell from Rexton, has interests which include ceramics, best property developers in singapore developers in singapore and scrabble. Was especially enthused after visiting Alejandro de Humboldt National Park..

Algorithm types

Machine learning algorithms can be organized into a taxonomy based on the desired outcome of the algorithm or the type of input available during training the machine Potter or Ceramic Artist Truman Bedell from Rexton, has interests which include ceramics, best property developers in singapore developers in singapore and scrabble. Was especially enthused after visiting Alejandro de Humboldt National Park..

  • Supervised learning algorithms are trained on labelled examples, i.e., input where the desired output is known. The supervised learning algorithm attempts to generalise a function or mapping from inputs to outputs which can then be used to speculatively generate an output for previously unseen inputs.
  • Unsupervised learning algorithms operate on unlabelled examples, i.e., input where the desired output is unknown. Here the objective is to discover structure in the data (e.g. through a cluster analysis), not to generalise a mapping from inputs to outputs.
  • Semi-supervised learning combines both labeled and unlabelled examples to generate an appropriate function or classifier.
  • Transduction, or transductive inference, tries to predict new outputs on specific and fixed (test) cases from observed, specific (training) cases.
  • Reinforcement learning is concerned with how intelligent agents ought to act in an environment to maximise some notion of reward. The agent executes actions which cause the observable state of the environment to change. Through a sequence of actions, the agent attempts to gather knowledge about how the environment responds to its actions, and attempts to synthesise a sequence of actions that maximises a cumulative reward.
  • Learning to learn learns its own inductive bias based on previous experience.
  • Developmental learning, elaborated for Robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers, and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.

Theory

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The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common.

In addition to performance bounds, computational learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

There are many similarities between machine learning theory and statistical inference, although they use different terms.

Approaches

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Decision tree learning

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Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value.

Association rule learning

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Association rule learning is a method for discovering interesting relations between variables in large databases.

Artificial neural networks

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An artificial neural network (ANN) learning algorithm, usually called "neural network" (NN), is a learning algorithm that is inspired by the structure and functional aspects of biological neural networks. Computations are structured in terms of an interconnected group of artificial neurons, processing information using a connectionist approach to computation. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs, to find patterns in data, or to capture the statistical structure in an unknown joint probability distribution between observed variables.

Inductive logic programming

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Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program which entails all the positive and none of the negative examples. Inductive programming is a related field that considers any kind of programming languages for representing hypotheses (and not only logic programming), such as functional programs.

Support vector machines

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Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.

Clustering

Mining Engineer (Excluding Oil ) Truman from Alma, loves to spend time knotting, largest property developers in singapore developers in singapore and stamp collecting. Recently had a family visit to Urnes Stave Church. Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated for example by internal compactness (similarity between members of the same cluster) and separation between different clusters. Other methods are based on estimated density and graph connectivity. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis.

Bayesian networks

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A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning.

Reinforcement learning

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Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. Reinforcement learning differs from the supervised learning problem in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected.

Representation learning

Several learning algorithms, mostly unsupervised learning algorithms, aim at discovering better representations of the inputs provided during training. Classical examples include principal components analysis and cluster analysis. Representation learning algorithms often attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions, allowing to reconstruct the inputs coming from the unknown data generating distribution, while not being necessarily faithful for configurations that are implausible under that distribution. Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse (has many zeros). Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into (high-dimensional) vectors.[7] Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.[8]

Similarity and metric learning

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In this problem, the learning machine is given pairs of examples that are considered similar and pairs of less similar objects. It then needs to learn a similarity function (or a distance metric function) that can predict if new objects are similar. It is sometimes used in Recommendation systems.

Sparse Dictionary Learning

In this method, a datum is represented as a linear combination of basis functions, and the coefficients are assumed to be sparse. Let x be a d-dimensional datum, D be a d by n matrix, where each column of D represents a basis function. r is the coefficient to represent x using D. Mathematically, sparse dictionary learning means the following x≈D×r where r is sparse. Generally speaking, n is assumed to be larger than d to allow the freedom for a sparse representation.

Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine which classes a previously unseen datum belongs to. Suppose a dictionary for each class has already been built. Then a new datum is associated with the class such that it's best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image path can be sparsely represented by an image dictionary, but the noise cannot.[9]

Applications

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This is one of the only things that require you to spend a little money to make money. Just go make an account, get a paypal account, and start selling. To go one step beyond just affiliating products and services is to create your own and sell it through your blog. Not great if you really enjoy trying out all the themes. Talking in real time having a real person causes it to be personal helping me personally to sort out how to proceed. The first step I took was search for a discount code, as I did with HostGator. Using a HostGator coupon is a beneficial method to get started. As long as the necessities are able to preserve the horizontal functionality of your site, you would pretty much be fine. Applications for machine learning include:

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In 2006, the online movie company Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[12]

In 2010 The Wall Street Journal wrote about a money management firm Rebellion Research's use of machine learning to predict economic movements, the article talks about Rebellion Research's prediction of the financial crisis and economic recovery.[13]

Software

Ayasdi, Angoss KnowledgeSTUDIO, Apache Mahout, Gesture Recognition Toolkit, IBM SPSS Modeler, KNIME, KXEN Modeler, LIONsolver, MATLAB, mlpy, MLPACK library, MCMLL, OpenCV, dlib, Oracle Data Mining, Orange, Python scikit-learn, R, RapidMiner, Salford Predictive Modeler, SAS Enterprise Miner, Shogun toolbox, STATISTICA Data Miner, and Weka are software suites containing a variety of machine learning algorithms.

Journals and conferences

See also

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References

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Further reading

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  • Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012). Foundations of Machine Learning, The MIT Press. ISBN 9780262018258.
  • Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664pp., ISBN 978-0123748560.
  • Sergios Theodoridis, Konstantinos Koutroumbas (2009) "Pattern Recognition", 4th Edition, Academic Press, ISBN 978-1-59749-272-0.
  • Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.
  • Bing Liu (2007), Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, ISBN 3-540-37881-2
  • Toby Segaran (2007), Programming Collective Intelligence, O'Reilly, ISBN 0-596-52932-5
  • Huang T.-M., Kecman V., Kopriva I. (2006), Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 260 pp. 96 illus., Hardcover, ISBN 3-540-31681-7.
  • Ethem Alpaydın (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, ISBN 0-262-01211-1
  • MacKay, D.J.C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. ISBN 0-521-64298-1.
  • KECMAN Vojislav (2001), Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA, 608 pp., 268 illus., ISBN 0-262-11255-8.
  • Trevor Hastie, Robert Tibshirani and Jerome Friedman (2001). The Elements of Statistical Learning, Springer. ISBN 0-387-95284-5.
  • Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3.
  • Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864-2.
  • Ryszard S. Michalski, George Tecuci (1994), Machine Learning: A Multistrategy Approach, Volume IV, Morgan Kaufmann, ISBN 1-55860-251-8.
  • Sholom Weiss and Casimir Kulikowski (1991). Computer Systems That Learn, Morgan Kaufmann. ISBN 1-55860-065-5.
  • Yves Kodratoff, Ryszard S. Michalski (1990), Machine Learning: An Artificial Intelligence Approach, Volume III, Morgan Kaufmann, ISBN 1-55860-119-8.
  • Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1986), Machine Learning: An Artificial Intelligence Approach, Volume II, Morgan Kaufmann, ISBN 0-934613-00-1.
  • Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1983), Machine Learning: An Artificial Intelligence Approach, Tioga Publishing Company, ISBN 0-935382-05-4.
  • Vladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, ISBN 0-471-03003-1.
  • Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56-62, 1957.
  • Ray Solomonoff, "An Inductive Inference Machine" A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.

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External links

  1. ↑ Wernick, Yang, Brankov, Yourganov and Strother, Machine Learning in Medical Imaging, IEEE Signal Processing Magazine, vol. 27, no. 4, July 2010, pp. 25-38
  2. ↑ 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

    My blog: http://www.primaboinca.com/view_profile.php?userid=5889534
  3. ↑ * Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7, p.2.
  4. ↑ Many property agents need to declare for the PIC grant in Singapore. However, not all of them know find out how to do the correct process for getting this PIC scheme from the IRAS. There are a number of steps that you need to do before your software can be approved.

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  5. ↑ Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer ISBN 0-387-31073-8.
  6. ↑ Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012) Foundations of Machine Learning, The MIT Press ISBN 9780262018258.
  7. ↑ One of the biggest reasons investing in a Singapore new launch is an effective things is as a result of it is doable to be lent massive quantities of money at very low interest rates that you should utilize to purchase it. Then, if property values continue to go up, then you'll get a really high return on funding (ROI). Simply make sure you purchase one of the higher properties, reminiscent of the ones at Fernvale the Riverbank or any Singapore landed property Get Earnings by means of Renting

    In its statement, the singapore property listing - website link, government claimed that the majority citizens buying their first residence won't be hurt by the new measures. Some concessions can even be prolonged to chose teams of consumers, similar to married couples with a minimum of one Singaporean partner who are purchasing their second property so long as they intend to promote their first residential property. Lower the LTV limit on housing loans granted by monetary establishments regulated by MAS from 70% to 60% for property purchasers who are individuals with a number of outstanding housing loans on the time of the brand new housing purchase. Singapore Property Measures - 30 August 2010 The most popular seek for the number of bedrooms in Singapore is 4, followed by 2 and three. Lush Acres EC @ Sengkang

    Discover out more about real estate funding in the area, together with info on international funding incentives and property possession. Many Singaporeans have been investing in property across the causeway in recent years, attracted by comparatively low prices. However, those who need to exit their investments quickly are likely to face significant challenges when trying to sell their property – and could finally be stuck with a property they can't sell. Career improvement programmes, in-house valuation, auctions and administrative help, venture advertising and marketing, skilled talks and traisning are continuously planned for the sales associates to help them obtain better outcomes for his or her shoppers while at Knight Frank Singapore. No change Present Rules

    Extending the tax exemption would help. The exemption, which may be as a lot as $2 million per family, covers individuals who negotiate a principal reduction on their existing mortgage, sell their house short (i.e., for lower than the excellent loans), or take part in a foreclosure course of. An extension of theexemption would seem like a common-sense means to assist stabilize the housing market, but the political turmoil around the fiscal-cliff negotiations means widespread sense could not win out. Home Minority Chief Nancy Pelosi (D-Calif.) believes that the mortgage relief provision will be on the table during the grand-cut price talks, in response to communications director Nadeam Elshami. Buying or promoting of blue mild bulbs is unlawful.

    A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running

    The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more

    There are various methods to go about discovering the precise property. Some local newspapers (together with the Straits Instances ) have categorised property sections and many local property brokers have websites. Now there are some specifics to consider when buying a 'new launch' rental. Intended use of the unit Every sale begins with 10 p.c low cost for finish of season sale; changes to 20 % discount storewide; follows by additional reduction of fiftyand ends with last discount of 70 % or extra. Typically there is even a warehouse sale or transferring out sale with huge mark-down of costs for stock clearance. Deborah Regulation from Expat Realtor shares her property market update, plus prime rental residences and houses at the moment available to lease Esparina EC @ Sengkang
  8. ↑ 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

    My blog: http://www.primaboinca.com/view_profile.php?userid=5889534
  9. ↑ Aharon, M, M Elad, and A Bruckstein. 2006. “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation.” Signal Processing, IEEE Transactions on 54 (11): 4311-4322
  10. ↑ 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

    My blog: http://www.primaboinca.com/view_profile.php?userid=5889534
  11. ↑ One of the biggest reasons investing in a Singapore new launch is an effective things is as a result of it is doable to be lent massive quantities of money at very low interest rates that you should utilize to purchase it. Then, if property values continue to go up, then you'll get a really high return on funding (ROI). Simply make sure you purchase one of the higher properties, reminiscent of the ones at Fernvale the Riverbank or any Singapore landed property Get Earnings by means of Renting

    In its statement, the singapore property listing - website link, government claimed that the majority citizens buying their first residence won't be hurt by the new measures. Some concessions can even be prolonged to chose teams of consumers, similar to married couples with a minimum of one Singaporean partner who are purchasing their second property so long as they intend to promote their first residential property. Lower the LTV limit on housing loans granted by monetary establishments regulated by MAS from 70% to 60% for property purchasers who are individuals with a number of outstanding housing loans on the time of the brand new housing purchase. Singapore Property Measures - 30 August 2010 The most popular seek for the number of bedrooms in Singapore is 4, followed by 2 and three. Lush Acres EC @ Sengkang

    Discover out more about real estate funding in the area, together with info on international funding incentives and property possession. Many Singaporeans have been investing in property across the causeway in recent years, attracted by comparatively low prices. However, those who need to exit their investments quickly are likely to face significant challenges when trying to sell their property – and could finally be stuck with a property they can't sell. Career improvement programmes, in-house valuation, auctions and administrative help, venture advertising and marketing, skilled talks and traisning are continuously planned for the sales associates to help them obtain better outcomes for his or her shoppers while at Knight Frank Singapore. No change Present Rules

    Extending the tax exemption would help. The exemption, which may be as a lot as $2 million per family, covers individuals who negotiate a principal reduction on their existing mortgage, sell their house short (i.e., for lower than the excellent loans), or take part in a foreclosure course of. An extension of theexemption would seem like a common-sense means to assist stabilize the housing market, but the political turmoil around the fiscal-cliff negotiations means widespread sense could not win out. Home Minority Chief Nancy Pelosi (D-Calif.) believes that the mortgage relief provision will be on the table during the grand-cut price talks, in response to communications director Nadeam Elshami. Buying or promoting of blue mild bulbs is unlawful.

    A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running

    The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more

    There are various methods to go about discovering the precise property. Some local newspapers (together with the Straits Instances ) have categorised property sections and many local property brokers have websites. Now there are some specifics to consider when buying a 'new launch' rental. Intended use of the unit Every sale begins with 10 p.c low cost for finish of season sale; changes to 20 % discount storewide; follows by additional reduction of fiftyand ends with last discount of 70 % or extra. Typically there is even a warehouse sale or transferring out sale with huge mark-down of costs for stock clearance. Deborah Regulation from Expat Realtor shares her property market update, plus prime rental residences and houses at the moment available to lease Esparina EC @ Sengkang
  12. ↑ "BelKor Home Page" research.att.com
  13. ↑ [1]
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