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| '''Preference learning''' is a subfield in [[machine learning]] in which the goal is to learn a predictive [[Preference (economics)|preference]] model from observed preference information.<ref>[[Mehryar Mohri]], Afshin Rostamizadeh, Ameet Talwalkar (2012) ''Foundations of Machine Learning'', The
| | Leverage gives us the option to control a lot of money with just a small percentage of that money being ours. When trading stocks, if you want to buy $1,000 of stock, you must invest at least $500. This gives you a leverage of 2:1.<br><br>The next most popular questions were on how one gets started trading stocks. Where can you learn what you need to know, and what do you need to do to get started?<br><br>The US government has continued to mint out more and more money despite of accruing annual deficits. This move is devaluing the Dollar and many financial analysts foresee a hyperinflation of the US economy soon. From the look of things buying silver or investing in stocks is safer than having cash that continues to lose value.<br><br>Due to the jobless rate, many homes have gone into foreclosure or signed back to the mortgage company due to bankruptcy. Buying foreclosed homes or those available through the short sale process can be bought for half their normal purchase price. Low interest rates of 5% or less will also keep payments low until the home can either be resold for a profit or [http://news.goldgrey.org/category/gold/ http://news.goldgrey.org/category/gold/] rented out.<br><br>Basically a stock trading community is a community of traders who buy or sell products through internet facilities. Most of the members of such communities make use powerful tools like "chat" and "Forum". Such tools increase the efficiency of the mode of communications among the members of the community. Apart from from these tools, there is another popular tool which is 'blog'. The uses of blogs in various stock trading communities. You can also put your views on the trading websites by means of blogs. These are informative tools which would definitely enrich your stock trading knowledge.<br><br>When it comes to investing real estate, it is more than a place you own or a place where you live, an added building or land wherein you are earning profit, or you are gaining. Depending on how you invest. Some purchased more than one property for the purpose of living and others to earn by looking for tenants. The place or where you're putting that actual investment is what you call as real estate, and if your purpose is to gain profit out of it, you are already investing. It is beyond a home to live but it is more of an investment.<br><br>When you go online and start researching stock trading basics, you can learn what you need to become very successful. Make it what you want it to be. Make money be trading stocks on the stock market by learning all that you can about the stock market. You will be glad that you did. |
| MIT Press ISBN 9780262018258.</ref> In the view of [[supervised learning]], preference learning trains on a set of items which have preferences toward labels or other items and predicts the preferences for all items.
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| While the concept of preference learning has been emerged for some time in many fields such as [[economics]],<ref name="SHOG00" /> it's a relatively new topic in [[Artificial Intelligence]] research. Several workshops have been discussing preference learning and related topics in the past decade.<ref name="WEB:WORKSHOP" />
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| ==Tasks==
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| The main task in preference learning concerns problems in "[[learning to rank]]". According to different types of preference information observed, the tasks are categorized as three main problems in the book ''Preference Learning'':<ref name="FURN11" /> | |
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| ===Label ranking===
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| In label ranking, the model has an instance space <math>X=\{x_i\}\,\!</math> and a finite set of labels <math>Y=\{y_i|i=1,2,\cdots,k\}\,\!</math>. The preference information is given in the form <math>y_i \succ_{x} y_j\,\!</math> indicating instance <math>x\,\!</math> shows preference in <math>y_i\,\!</math> rather than <math>y_j\,\!</math>. A set of preference information is used as training data in the model. The task of this model is to find a preference ranking among the labels for any instance.
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| It was observed some conventional [[Classification in machine learning|classification]] problems can be generalized in the framework of label ranking problem:<ref name="HARP03" /> if a training instance <math>x\,\!</math> is labeled as class <math>y_i\,\!</math>, it implies that <math>\forall j \neq i, y_i \succ_{x} y_j\,\!</math>. In [[Multi-label classification|multi-label]] situation, <math>x\,\!</math> is associated with a set of labels <math>L \subseteq Y\,\!</math> and thus the model can extract a set of preference information <math>\{y_i \succ_{x} y_j | y_i \in L, y_j \in Y\backslash L\}\,\!</math>. Training a preference model on this preference information and the classification result of an instance is just the corresponding top ranking label.
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| ===Instance ranking===
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| Instance ranking also has the instance space <math>X\,\!</math> and label set <math>Y\,\!</math>. In this task, labels are defined to have a fixed order <math>y_1 \succ y_2 \succ \cdots \succ y_k\,\!</math> and each instance <math>x_l\,\!</math> is associated with a label <math>y_l\,\!</math>. Giving a set of instances as training data, the goal of this task is to find the ranking order for a new set of instances.
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| ===Object ranking===
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| Object ranking is similar to instance ranking except that no labels are associated with instances. Given a set of pairwise preference information in the form <math>x_i \succ x_j\,\!</math> and the model should find out a ranking order among instances.
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| ==Techniques==
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| There are two practical representations of the preference information <math>A \succ B\,\!</math>. One is assigning <math>A\,\!</math> and <math>B\,\!</math> with two real numbers <math>a\,\!</math> and <math>b\,\!</math> respectively such that <math>a > b\,\!</math>. Another one is assigning a binary value <math>V(A,B) \in \{0,1\}\,\!</math> for all pairs <math>(A,B)\,\!</math> denoting whether <math>A \succ B\,\!</math> or <math>B \succ A\,\!</math>. Corresponding to these two different representations, there are two different techniques applied to the learning process.
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| ===Utility function===
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| If we can find a mapping from data to real numbers, ranking the data can be solved by ranking the real numbers. This mapping is called [[utility function]]. For label ranking the mapping is a function <math>f: X \times Y \rightarrow \mathbb{R}\,\!</math> such that <math>y_i \succ_x y_j \Rightarrow f(x,y_i) > f(x,y_j)\,\!</math>. For instance ranking and object ranking, the mapping is a function <math>f: X \rightarrow \mathbb{R}\,\!</math>.
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| Finding the utility function is a [[Regression analysis|regression]] learning problem which is well developed in machine learning.
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| ===Preference relations===
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| The binary representation of preference information is called preference relation. For each pair of alternatives (instances or labels), a binary predicate can be learned by conventional supervising learning approach. Fürnkranz, Johannes and Hüllermeier proposed this approach in label ranking problem.<ref name="FURN03" /> For object ranking, there is an early approach by Cohen et al.<ref name="COHE98" /> | |
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| Using preference relations to predict the ranking will not be so intuitive. Since preference relation is not transitive, it implies that the solution of ranking satisfying those relations would sometimes be unreachable, or there could be more than one solution. A more common approach is to find a ranking solution which is maximally consistent with the preference relations. This approach is a natural extension of pairwise classification.<ref name="FURN03" />
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| ==Uses==
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| Preference learning can be used in ranking search results according to feedback of user preference. Given a query and a set of documents, a learning model is used to find the ranking of documents corresponding to the relevance with this query. More discussions on research in this field can be found in Tie-Yan Liu's survey paper.<ref name="LIU09" />
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| Another application of preference learning is [[recommender systems]].<ref name="GEMM09" /> Online store may analyze customer's purchase record to learn a preference model and then recommend similar products to customers. Internet content providers can make use of user's ratings to provide more user preferred contents.
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| ==See also==
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| *[[Learning to rank]]
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| ==References==
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| {{Reflist|
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| refs=
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| <ref name="SHOG00">{{
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| cite journal
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| |last = Shogren
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| |first = Jason F.
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| |coauthors = List, John A.; Hayes, Dermot J.
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| |year = 2000
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| |title = Preference Learning in Consecutive Experimental Auctions
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| |url = http://ideas.repec.org/a/bla/ajagec/v82y2000i4p1016-21.html
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| |journal = American Journal of Agricultural Economics
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| |volume = 82
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| |pages = 1016–1021
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| }}</ref>
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| <ref name="WEB:WORKSHOP">{{
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| cite web
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| |title = Preference learning workshops
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| |url = http://www.preference-learning.org/#Workshops
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| }}</ref>
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| <ref name="FURN11">{{
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| cite book
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| |last = Fürnkranz
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| |first = Johannes
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| |coauthors = Hüllermeier, Eyke
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| |year = 2011
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| |title = Preference Learning
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| |url = http://books.google.com/books?id=nc3XcH9XSgYC
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| |chapter = Preference Learning: An Introduction
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| |chapterurl = http://books.google.com/books?id=nc3XcH9XSgYC&pg=PA4
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| |publisher = Springer-Verlag New York, Inc.
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| |pages = 3–8
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| |isbn = 978-3-642-14124-9
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| }}</ref>
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| <ref name="HARP03">{{ | |
| cite journal
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| |last = Har-peled
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| |first = Sariel
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| |coauthors = Roth, Dan; Zimak, Dav
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| |year = 2003
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| |title = Constraint classification for multiclass classification and ranking
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| |journal = In Proceedings of the 16th Annual Conference on Neural Information Processing Systems, NIPS-02
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| |pages = 785–792
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| }}</ref>
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| <ref name="FURN03">{{
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| cite journal
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| |last = Fürnkranz
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| |first = Johannes
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| |coauthors = Hüllermeier, Eyke
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| |year = 2003
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| |title = Pairwise Preference Learning and Ranking
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| |journal = Proceedings of the 14th European Conference on Machine Learning
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| |pages = 145–156
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| }}</ref>
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| <ref name="COHE98">{{
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| cite journal
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| |last = Cohen
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| |first = William W.
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| |coauthors = Schapire, Robert E.; Singer, Yoram
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| |year = 1998
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| |title = Learning to order things
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| |url = http://dl.acm.org/citation.cfm?id=302528.302736
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| |journal = In Proceedings of the 1997 Conference on Advances in Neural Information Processing Systems
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| |pages = 451–457
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| }}</ref>
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| <ref name="LIU09">{{
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| cite journal
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| |last = Liu
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| |first = Tie-Yan
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| |year = 2009
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| |title = Learning to Rank for Information Retrieval
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| |url = http://dl.acm.org/citation.cfm?id=1618303.1618304
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| |journal = Foundations and Trends in Information Retrieval
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| |volume = 3
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| |issue = 3
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| |pages = 225–331
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| |doi = 10.1561/1500000016
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| }}</ref>
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| <ref name="GEMM09">{{
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| cite journal
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| |last = Gemmis
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| |first = Marco De
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| |coauthors = Iaquinta, Leo; Lops, Pasquale; Musto, Cataldo; Narducci, Fedelucio; Semeraro,Giovanni
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| |year = 2009
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| |title = Preference Learning in Recommender Systems
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| |url = http://www.ecmlpkdd2009.net/wp-content/uploads/2008/09/preference-learning.pdf#page=45
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| |journal = PREFERENCE LEARNING
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| |volume = 41
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| |pages = 387–407
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| }}</ref>
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| }}
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| ==External links==
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| *[http://www.preference-learning.org/ Preference Learning site]
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| [[Category:Information retrieval]]
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| [[Category:Machine learning]]
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Leverage gives us the option to control a lot of money with just a small percentage of that money being ours. When trading stocks, if you want to buy $1,000 of stock, you must invest at least $500. This gives you a leverage of 2:1.
The next most popular questions were on how one gets started trading stocks. Where can you learn what you need to know, and what do you need to do to get started?
The US government has continued to mint out more and more money despite of accruing annual deficits. This move is devaluing the Dollar and many financial analysts foresee a hyperinflation of the US economy soon. From the look of things buying silver or investing in stocks is safer than having cash that continues to lose value.
Due to the jobless rate, many homes have gone into foreclosure or signed back to the mortgage company due to bankruptcy. Buying foreclosed homes or those available through the short sale process can be bought for half their normal purchase price. Low interest rates of 5% or less will also keep payments low until the home can either be resold for a profit or http://news.goldgrey.org/category/gold/ rented out.
Basically a stock trading community is a community of traders who buy or sell products through internet facilities. Most of the members of such communities make use powerful tools like "chat" and "Forum". Such tools increase the efficiency of the mode of communications among the members of the community. Apart from from these tools, there is another popular tool which is 'blog'. The uses of blogs in various stock trading communities. You can also put your views on the trading websites by means of blogs. These are informative tools which would definitely enrich your stock trading knowledge.
When it comes to investing real estate, it is more than a place you own or a place where you live, an added building or land wherein you are earning profit, or you are gaining. Depending on how you invest. Some purchased more than one property for the purpose of living and others to earn by looking for tenants. The place or where you're putting that actual investment is what you call as real estate, and if your purpose is to gain profit out of it, you are already investing. It is beyond a home to live but it is more of an investment.
When you go online and start researching stock trading basics, you can learn what you need to become very successful. Make it what you want it to be. Make money be trading stocks on the stock market by learning all that you can about the stock market. You will be glad that you did.