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

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.