Document Classification on Neural Networks Using Only Positive Examples
Larry Michael Manevitz, Malik Yousef · 2000
In this paper, we show how a simple feedforward neural network can be trained to filter documents when only positive information is available, and that this method seems to be superior to more standard methods, such as tf-idf retrieval based on an average vector. A novel experimental finding 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.