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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.
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.