Knowledge Representation and Organization in Machine Learning

ISBN-13: 9783540507680 / Angielski / Miękka / 1989 / 322 str.

Katharina Morik

Opis

Machine learning has become a growing field of Artificial Intelligence. This book contains research papers that focus on the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. It is of interest to both experts and newcomers to the subject.

Spis treści

Explanation: A source of guidance for knowledge representation.- (Re)presentation issues in second generation expert systems.- Some aspects of learning and reorganization in an analogical representation.- A knowledge-intensive learning system for document retrieval.- Constructing expert systems as building mental models or toward a cognitive ontology for expert systems.- Sloppy modeling.- The central role of explanations in disciple.- An inference engine for representing multiple theories.- The acquisition of model-knowledge for a model-driven machine learning approach.- Using attribute dependencies for rule learning.- Learning disjunctive concepts.- The use of analogy in incremental SBL.- Knowledge base refinement using apprenticeship learning techniques.- Creating high level knowledge structures from simple elements.- Demand-driven concept formation.