Document classification on neural networks using only positive examples (poster session)
Larry Michael Manevitz, Malik Yousef · 2000
In this paper, we show how a simple feed-forward 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.