<mods:mods version="3.3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mods:titleInfo><mods:title>ALEX: automatic learning in expert systems</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">L.</mods:namePart><mods:namePart type="family">Winkelbauer</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">K.</mods:namePart><mods:namePart type="family">Fedra</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods: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</mods:abstract><mods:originInfo><mods:dateIssued encoding="iso8601">1991</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>IEEE</mods:publisher></mods:originInfo><mods:genre>Book Section</mods:genre></mods:mods>