Amazon cover image
Image from Amazon.com

Knowledge representation and organization in machine learning / K. Morik (ed.).

Contributor(s): Material type: TextTextSeries: Lecture notes in computer science ; 347. | Lecture notes in computer science. Lecture notes in artificial intelligence.Publication details: Berlin ; New York : Springer-Verlag, ©1989.Description: 1 online resource (xiii, 319 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540460817
  • 3540460810
Subject(s): Genre/Form: Additional physical formats: Print version:: Knowledge representation and organization in machine learning.DDC classification:
  • 006.3/1 19
LOC classification:
  • Q325 .K6 1989
Other classification:
  • 54.72
  • *68-06
  • 28
  • 68T05
  • 68T99
Online resources:
Contents:
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.
Action note:
  • digitized 2010 HathiTrust Digital Library committed to preserve
Summary: Machine learning has become a rapidly growing field of Artificial Intelligence. Since the First International Workshop on Machine Learning in 1980, the number of scientists working in the field has been increasing steadily. This situation allows for specialization within the field. There are two types of specialization: on subfields or, orthogonal to them, on special subjects of interest. This book follows the thematic orientation. It contains research papers, each of which throws light upon the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. Building up appropriate representations is considered to be the main concern of knowledge acquisition for knowledge-based systems throughout the book. Here machine learning is presented as a tool for building up such representations. But machine learning itself also states new representational problems. This book gives an easy-to-understand insight into a new field with its problems and the solutions it offers. Thus it will be of good use to both experts and newcomers to the subject.
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library eBook LNCS Available
Total holds: 0

Includes bibliographical references.

Print version record.

Use copy Restrictions unspecified star MiAaHDL

Electronic reproduction. [Place of publication not identified] : HathiTrust Digital Library, 2010. MiAaHDL

Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. MiAaHDL

http://purl.oclc.org/DLF/benchrepro0212

digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL

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.

Machine learning has become a rapidly growing field of Artificial Intelligence. Since the First International Workshop on Machine Learning in 1980, the number of scientists working in the field has been increasing steadily. This situation allows for specialization within the field. There are two types of specialization: on subfields or, orthogonal to them, on special subjects of interest. This book follows the thematic orientation. It contains research papers, each of which throws light upon the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. Building up appropriate representations is considered to be the main concern of knowledge acquisition for knowledge-based systems throughout the book. Here machine learning is presented as a tool for building up such representations. But machine learning itself also states new representational problems. This book gives an easy-to-understand insight into a new field with its problems and the solutions it offers. Thus it will be of good use to both experts and newcomers to the subject.

Powered by Koha