One-class Document Classification : One-class Document Classification via Neural Networks and Support Vector Machines

Malik Yousef

ISBN 10: 363914869X / ISBN 13: 9783639148695

Editore: VDM Verlag Dr. Müller, 2009

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nach der Bestellung gedruckt Neuware - Printed after ordering - In this book, we address the problem of automated information retrieval and document classification using only positive examples.In this book, we show how a simple feed-forward neural network can be trained to filter documents under these conditions,and that this method seems to be superior to modifiedmethods (modified to use only positive training examples),such as Rocchio, Nearest Neighbor,Naive-Bayes and Distance- based Probability algorithms.A novel experimental findingis that retrieval is enhanced substantiallyin this context by carrying out a certain kind of uniform transformation (Hadamard) of the information prior to the training of the network.We also implemented versions of support vector machine (SVM) appropriate for one-classclassificationin the context of information retrieval.Finally we present a system designed to help a user navigate the Web. The system is built upon neural network techniques designed to attack the problem of user modeling using only positive examples.

Codice articolo 9783639148695

Dati bibliografici

• Titolo: One-class Document Classification : One-class Document Classification via Neural Networks and Support Vector Machines
• Editore: VDM Verlag Dr. Müller
• Data di pubblicazione: 2009
• Lingua: Inglese
• ISBN 10: 363914869X
• ISBN 13: 9783639148695
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 176 A x 33 P (millimetri) 220 millimetri di larghezza per 176 millimetri di altezza per 33 millimetri di profondità
• Peso: 177 grammi

Descrizione articolo

Riassunto

In this book, we address the problem of automated information retrieval and document classification using only positive examples.In this book, we show how a simple feed-forward neural network can be trained to filter documents under these conditions, and that this method seems to be superior to modified methods (modified to use only positive training examples),such as Rocchio, Nearest Neighbor, Naive-Bayes and Distance- based Probability algorithms.A novel experimental finding is that retrieval is enhanced substantially in this context by carrying out a certain kind of uniform transformation (Hadamard) of the information prior to the training of the network. We also implemented versions of support vector machine (SVM) appropriate for one-classclassification in the context of information retrieval. Finally we present a system designed to help a user navigate the Web. The system is built upon neural network techniques designed to attack the problem of user modeling using only positive examples.

L'autore

Malik Yousef was born in Dabburia Village, Israel. In 2001, he received his Ph.D. in Computer Science and Mathematics from the Haifa University, Israel. In 2004. He joined the Showe Laboratory at the Wistar Institute in Philadelphia, USA as a Post-Doctoral Fellow. His research interests include Machine Learning and Computational Biology.