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Record identifier : 568128
Personal Name - Primary Intelectual Responsibility : Aminian, Minoo
Title and statement of responsibility : Active learning with partially-labeled data to reduce classification loss [Thesis]
Publication, Distribution,Etc. : State University of New York at Albany, 2006
Language of the Item : eng
Dissertation of thesis details and type of degree : Ph.D.
Body granting the degree : , State University of New York at Albany
Summary or Abstract : In many occasions in real life, we are faced with the problem of classification of partially labeled data, or semi-supervised learning. We consider the special case of scarcely labeled data or when the labeled data is insufficient, and present a principled method which implements active learning in scarcely labeled data to enhance the performance of the learner. Our active learning algorithm is a co-training procedure that does not require natural view splits. Blum and Mitchell's co-training is a popular semi-supervised algorithm to use when we have multiple independent views of the entities to classify. An example of a multi-view situation is classifying web pages: one view may describe the pages by the words that occur on them, another view describes the pages by the words in the hyperlinks that point to them. So we start by analyzing co-training procedure and then introduce our general active learning algorithm..
Topical Name Used as Subject : Computer science
Information of biblio record : TL
 
 
 
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