Machine learning approach to document retrieval: An overview and an experiment

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this article we first provide an overview of AI techniques and then present a machine learning based document retrieval system we developed. GANNET (Genetic Algorithms and Neural Nets System) performed concept (keyword) optimization for user-selected documents during document retrieval using the genetic algorithms. It then used the optimized concepts to perform concept exploration in a large network of related concepts through the Hopfield net parallel relaxation procedure. Our preliminary experiment showed that GANNET helped improve search recall by identifying the underlying concepts (keywords) which best describe the user-selected documents.

Original languageEnglish (US)
Title of host publicationProceedings of the Hawaii International Conference on System Sciences
EditorsJay F. Nunamaker, Ralph H.Jr. Sprague
PublisherPubl by IEEE
Pages631-640
Number of pages10
ISBN (Print)0818650702
StatePublished - Jan 1 1994
EventProceedings of the 27th Hawaii International Conference on System Sciences (HICSS-27). Part 4 (of 5) - Wailea, HI, USA
Duration: Jan 4 1994Jan 7 1994

Publication series

NameProceedings of the Hawaii International Conference on System Sciences
Volume3
ISSN (Print)1060-3425

Other

OtherProceedings of the 27th Hawaii International Conference on System Sciences (HICSS-27). Part 4 (of 5)
CityWailea, HI, USA
Period1/4/941/7/94

ASJC Scopus subject areas

  • Computer Science(all)

Fingerprint Dive into the research topics of 'Machine learning approach to document retrieval: An overview and an experiment'. Together they form a unique fingerprint.

  • Cite this

    Chen, H. (1994). Machine learning approach to document retrieval: An overview and an experiment. In J. F. Nunamaker, & R. H. J. Sprague (Eds.), Proceedings of the Hawaii International Conference on System Sciences (pp. 631-640). (Proceedings of the Hawaii International Conference on System Sciences; Vol. 3). Publ by IEEE.