One-class Document Classification
One-class Document Classification via Neural Networks and Support Vector Machines
Versandkostenfrei!
Versandfertig in 6-10 Tagen
32,99 €
inkl. MwSt.
PAYBACK Punkte
16 °P sammeln!
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 (Hadam...
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.
Andere Kunden interessierten sich für
