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Knowledge acquisition and machine learning : theory, methods, and applications

Knowledge acquisition and machine learning : theory, methods, and applications

자료유형
단행본
개인저자
Morik, Katharina.
서명 / 저자사항
Knowledge acquisition and machine learning : theory, methods, and applications / Katharina Morik ... [et al.].
발행사항
London ;   San Diego :   Academic Press,   c1933 [i.e. 1993].  
형태사항
xviii, 305 p. : ill. ; 24 cm.
총서사항
Knowledge-based systems.
ISBN
0125062303
서지주기
Includes bibliographical references (p. 281-294) and indexes.
일반주제명
Knowledge acquisition (Expert systems). Machine learning.
비통제주제어
Artificial intelligence,,
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049 1 ▼l 121001157 ▼f 과학
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245 0 0 ▼a Knowledge acquisition and machine learning : ▼b theory, methods, and applications / ▼c Katharina Morik ... [et al.].
260 ▼a London ; ▼a San Diego : ▼b Academic Press, ▼c c1933 [i.e. 1993].
300 ▼a xviii, 305 p. : ▼b ill. ; ▼c 24 cm.
490 1 ▼a Knowledge-based systems.
504 ▼a Includes bibliographical references (p. 281-294) and indexes.
650 0 ▼a Knowledge acquisition (Expert systems).
650 0 ▼a Machine learning.
653 0 ▼a Artificial intelligence
700 1 ▼a Morik, Katharina.
830 0 ▼a Knowledge-based systems (London, England)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.33 K732 등록번호 121001157 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

책소개

For graduate-/research- level students and professors, this book integrates machine learning with knowledge acquisition to overcome the problems of building models for knowledge-based systems to maintain them successfully. It also reports on BLIP and MOBAL systems developed over the last decade, which illustrate a particular way of unifying knowledge acquisition and machine learning. Practically-orientated, theoretical skills have been used and tested in real-world applications.



Feature

  • Integrates machine learning with knowledge acquisition to overcome the problems of building models for knowledge based systems to maintain them successfully
  • Reports on BLIP and MOBAL systems that have been developed over the past 10 years, which illustrate a particular way of unifying knowledge acquisition and machine learning
  • Practically oriented--theoretical results have been used and tested in real-world applications from the start



정보제공 : Aladin

목차

The Knowledge Acquisition Framework. The Knowledge Representation Environment. The Inference Im-2. The Sort Taxonomy. The Predicate Structure. Model-Driven Rule Discovery. Knowledge Revision. Concept Formation. Practical Experiences. Bibliography. Author Index. Name Index. Subject Index.


정보제공 : Aladin

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