G topic in vector space classicaon this set

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Unformatted text preview: space? 3 Introduc)on to Informa)on Retrieval Sec.14.1 Classifica)on Using Vector Spaces 4 Introduc)on to Informa)on Retrieval Sec.14.1 Documents in a Vector Space   As before, the training set is a set of documents, each labeled with its class (e.g., topic)   In vector space classifica)on, this set corresponds to a labeled set of points (or, equivalently, vectors) in the vector space   Premise 1: Documents in the same class form a con)guous region of space   Premise 2: Documents from different classes don’t overlap (much)   We define surfaces to delineate classes in the space Government Science Arts 5 6 1 Introduc)on to Informa)on Retrieval Sec.14.1 Test Document of what class? Introduc)on to Informa)on Retrieval Sec.14.1 Test Document = Government Is this similarity hypothesis true in general? Government Government Science Science Arts Arts 7 Introduc)on to Informa)on Retrieval Sec.14.1 Our main topic today is how to find good separators Introduc)on to Informa)on Retrieval 8 Sec.14.2 Using Rocchio for text classifica)on Aside: 2D/3D graphs can be misleading   Relevance feedback methods can be adapted for text categoriza)on   As noted before, relevance feedback can be viewed as 2 ­class classifica)on   Relevant vs. nonrelevant documents   Use standard h ­idf weighted vectors to represent text documents   For training documents in each category, compute a prototype vector by summing the vectors of the training documents in the category.   Prototype = centroid of...
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