eprintid: 13612 rev_number: 8 eprint_status: archive userid: 353 dir: disk0/00/01/36/12 datestamp: 2016-08-05 12:04:31 lastmod: 2021-08-27 17:41:27 status_changed: 2016-08-05 12:04:31 type: book_section metadata_visibility: show item_issues_count: 2 creators_name: Winkelbauer, L. creators_name: Fedra, K. creators_id: 1100 creators_id: 1049 title: ALEX: automatic learning in expert systems ispublished: pub abstract: An environment for automatic learning (ALEX) has been designed and implemented in a modular fashion. The system consists of an example generation module (i.e., tutor software system representing the application domain); the learning subsystem; an analysis component; and the user interface and control structure integrating these components. As the core of the learning subsystem the incremental learning algorithm ID-H has been developed, based on the incremental application of hybrid clustering. To improve the overall performance of the learning environment, a feedback loop between the results of a learning step and the input of the next learning step has been introduced. The learning environment can automatically direct its learning strategy according to its assessment of its performance date: 1991 date_type: published publisher: IEEE id_number: doi:10.1109/CAIA.1991.120846 official_url: http://dx.doi.org/10.1109/CAIA.1991.120846 creators_browse_id: 1668 creators_browse_id: 1241 full_text_status: none volume: i place_of_pub: Piscataway, NJ, United States pagerange: 59-62 refereed: TRUE book_title: Proceedings. The Seventh IEEE Conference on Artificial Intelligence Application coversheets_dirty: FALSE fp7_project: no fp7_type: info:eu-repo/semantics/bookPart citation: Winkelbauer, L. & Fedra, K. (1991). ALEX: automatic learning in expert systems. In: Proceedings. The Seventh IEEE Conference on Artificial Intelligence Application. pp. 59-62 Piscataway, NJ, United States: IEEE. 10.1109/CAIA.1991.120846 .