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| The '''Rocchio algorithm''' is based on a method of [[relevance feedback]] found in [[information retrieval]] systems which stemmed from the [[SMART Information Retrieval System]] around the year 1970. Like many other retrieval systems, the Rocchio feedback approach was developed using the [[Vector Space Model]]. The [[algorithm]] is based on the assumption that most users have a general conception of which documents should be denoted as [[Relevance (information retrieval)|relevant]] or non-relevant.<ref>Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: ''An Introduction to Information Retrieval'', page 181. Cambridge University Press, 2009.</ref> Therefore, the user's search query is revised to include an arbitrary percentage of relevant and non-relevant documents as a means of increasing the [[search engine]]'s [[Information_retrieval#Recall|recall]], and possibly the precision as well. The number of relevant and non-relevant documents allowed to enter a [[Information retrieval|query]] is dictated by the weights of the a, b, c variables listed below in the [[Rocchio_Classification#Algorithm|Algorithm section]].<ref>Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: ''An Introduction to Information Retrieval'', page 292. Cambridge University Press, 2009.</ref>
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| ==Algorithm==
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| The [[Formula (mathematical logic)|formula]] and variable definitions for Rocchio relevance feedback is as follows:<ref>Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: ''An Introduction to Information Retrieval'', page 182. Cambridge University Press, 2009.</ref>
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| <math> \overrightarrow{Q_m} = \bigl(a \cdot \overrightarrow{Q_o}\bigr) + \biggl(b \cdot {\tfrac{1}{|D_r|}} \cdot \sum_{\overrightarrow{D_j} \in D_r} \overrightarrow{D_j}\biggr)
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| - \biggl(c \cdot {\tfrac{1}{|D_{nr}|}} \cdot \sum_{\overrightarrow{D_k} \in D_{nr}} \overrightarrow{D_k}\biggr) </math>
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| {| class="wikitable"
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| |-
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| ! Variable
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| ! Value
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| |-
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| | <math> \overrightarrow{Q_m} </math>
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| | Modified Query Vector
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| |-
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| | <math> \overrightarrow{Q_o} </math>
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| | Original Query Vector
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| |-
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| | <math> \overrightarrow{D_j} </math>
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| | Related Document Vector
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| |-
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| | <math> \overrightarrow{D_k} </math>
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| | Non-Related Document Vector
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| |-
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| | <math> a </math>
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| | Original Query Weight
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| |-
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| | <math> b </math>
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| | Related Documents Weight
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| |-
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| | <math> c </math>
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| | Non-Related Documents Weight
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| |-
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| | <math> D_r </math>
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| | Set of Related Documents
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| |-
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| | <math> D_{nr} </math>
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| | Set of Non-Related Documents
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| |}
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| [[Image:Rocchioclassgraph.jpg|thumb|right|250px|Rocchio Classification]]
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| As demonstrated in the Rocchio formula, the associated weights ('''a''', '''b''', '''c''') are responsible for shaping the modified [[vector space|vector]] in a direction closer, or farther away, from the original query, related documents, and non-related documents. In particular, the values for '''b''' and '''c''' should be incremented or decremented proportionally to the set of documents classified by the user. If the user decides that the modified query should not contain terms from either the original query, related documents, or non-related documents, then the corresponding weight ('''a''', '''b''', '''c''') value for the category should be set to 0.
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| In the later part of the algorithm, the variables '''Dr''', and '''Dnr''' are presented to be sets of [[Tuple|vectors]] containing the coordinates of related documents and non-related documents. Though '''Dr''' and '''Dnr''' are not vectors themselves, <math> \overrightarrow{Dj} </math> and <math> \overrightarrow{Dk} </math> are the vectors used to iterate through the two sets and form vector [[summation]]s. These summations will be multiplied against the [[Multiplicative inverse]] of their respective document set ('''Dr''', '''Dnr''') to complete the addition or subtraction of related or non-related documents.
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| In order to visualize the changes taking place on the modified vector, please refer to the image below.<ref>Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: ''An Introduction to Information Retrieval'', page 293. Cambridge University Press, 2009.</ref> As the weights are increased or decreased for a particular category of documents, the coordinates for the modified vector begin to move either closer, or farther away, from the [[centroid]] of the document collection. Thus if the weight is increased for related documents, then the modified vectors [[coordinate]]s will reflect being closer to the centroid of related documents.
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| ==Time complexity==
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| The [[time complexity]] for training and testing the [[Rocchio_Classification#Algorithm|Rocchio Classification algorithm]] are listed below and followed by the definition of each [[variable (mathematics)|variable]]. Note that when in testing phase, the time complexity can be reduced to that of calculating the [[euclidean distance]] between a class [[centroid]] and the respective document. As shown by: <math>\Theta(\vert\mathbb{C}\vert M_{a})</math>.
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| Training = <math>\Theta(\vert\mathbb{D}\vert L_{ave}+\vert\mathbb{C}\vert\vert V\vert)</math> <br>
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| Testing = <math>\Theta( L_{a}+\vert\mathbb{C}\vert M_{a})= \Theta(\vert\mathbb{C}\vert M_{a})</math> <ref>Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: ''An Introduction to Information Retrieval'', page 296. Cambridge University Press, 2009.</ref>
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| {| class="wikitable"
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| |-
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| ! Variable
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| ! Value
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| |-
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| | <math> \mathbb{D} </math>
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| | Labeled Document Set
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| |-
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| | <math> L_{ave} </math>
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| | Average Tokens Per Document
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| |-
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| | <math> \mathbb{C} </math>
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| | Class Set
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| |-
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| | <math> V </math>
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| | Vocabulary/Term Set
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| |-
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| | <math> L_{a} </math>
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| | Number of Tokens in Document
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| |-
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| | <math> M_{a} </math>
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| | Number of Types in Document
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| |}
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| ==Usage== | |
| Though there are benefits to ranking documents as not-relevant, a [[relevant]] document ranking will result in more precise documents being made available to the user. Therefore, traditional values for the algorithm's weights ('''a''', '''b''', '''c''') in Rocchio Classification are typically around '''a = 1''', '''b = 0.8''', and ''' c = 0.1'''. Modern [[information retrieval]] systems have moved towards eliminating the non-related documents by setting '''c = 0''' and thus only accounting for related documents. Although not all [[Information retrieval|retrieval systems]] have eliminated the need for non-related documents, most have limited the effects on modified query by only accounting for strongest non-related documents in the '''Dnr''' set.
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| ==Limitations==
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| The Rocchio algorithm often fails to classify multimodal classes and relationships. For instance, the country of [[Burma]] was renamed to [[Myanmar]] in 1989. Therefore the two queries of "Burma" and "Myanmar" will appear much farther apart in the [[vector space model]], though they both contain similar origins.<ref>Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: ''An Introduction to Information Retrieval'', page 296. Cambridge University Press, 2009.</ref>
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| == See also ==
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| * [[Nearest centroid classifier]], aka Rocchio classifier
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| ==References==
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| {{reflist}}
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| * [http://nlp.stanford.edu/IR-book/pdf/09expand.pdf Relevance Feedback and Query Expansion]
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| * [http://nlp.stanford.edu/IR-book/pdf/14vcat.pdf Vector Space Classification]
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| * [http://cs.nyu.edu/courses/fall07/G22.2580-001/lec7.html Data Classification]
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| [[Category:Information retrieval]]
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CMS provides the best platform to create websites that fulfill all the specifications of SEO. You may discover this probably the most time-consuming part of building a Word - Press MLM website. These templates are professionally designed and are also Adsense ready. Transforming your designs to Word - Press blogs is not that easy because of the simplified way in creating your very own themes. It's as simple as hiring a Wordpress plugin developer or learning how to create what is needed.
As you know today Word - Press has turn out to be a tremendously popular open source publishing and blogging display place. If you wish to sell your services or products via internet using your website, you have to put together on the website the facility for trouble-free payment transfer between customers and the company. There are number of web services that offer Word press development across the world. This is identical to doing a research as in depth above, nevertheless you can see various statistical details like the number of downloads and when the template was not long ago updated. Once you've installed the program you can quickly begin by adding content and editing it with features such as bullet pointing, text alignment and effects without having to do all the coding yourself.
The entrepreneurs can easily captivate their readers by using these versatile themes. Browse through the popular Wordpress theme clubs like the Elegant Themes, Studio Press, Woo - Themes, Rocket Theme, Simple Themes and many more. You've got invested a great cope of time developing and producing up the topic substance. In crux the developer must have a detailed knowledge not only about the marketing tool but also about the ways in which it can be applied profitably. Search engine optimization pleasant picture and solution links suggest you will have a much better adjust at gaining considerable natural site visitors.
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