Document classification on neural networks using only positive examples

Larry M. Manevitz, Malik Yousef

Research output: Contribution to journalConference articlepeer-review


The training of feedforward neural networks for document filtering was discussed with respect to availability of positive information. A filter was developed for the search of the required document in a corpus of documents. A three-level feedforward neural network with a `bottleneck' was employed under standard back-propagation for learning the identity function. The term-frequency-inverse-document-frequency (tf-idf) and Hadamard product representations were employed for the implementation of the bottleneck filter.

Original languageEnglish
Pages (from-to)304-306
Number of pages3
JournalSIGIR Forum (ACM Special Interest Group on Information Retrieval)
StatePublished - 2000
EventProceedings of the 23rd International ACM SIGIR Conference on Research and Development in Infornation Retrieval (SIGIR 2000) - Athens, Greece
Duration: 24 Jul 200028 Jul 2000

ASJC Scopus subject areas

  • Management Information Systems
  • Hardware and Architecture


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