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Machine learning--EWSL-91 : proceedings

Machine learning--EWSL-91 : proceedings (1회 대출)

자료유형
단행본
개인저자
Kodratoff, Yves.
서명 / 저자사항
Machine learning--EWSL-91 : proceedings / European Working Session on Learning, Porto, Portugal, March 6-8, 1991 ; Y. Kodratoff (ed.).
발행사항
Berlin ;   New York :   Springer-Verlag,   c1991.  
형태사항
xi, 537 p. : ill. ; 25 cm.
총서사항
Lecture notes in computer science ; Lecture notes in artificial intelligence.482.
ISBN
354053816X (Springer-Verlag Berlin Heidelberg New York) 038753816X (Springer-Verlag New York Berlin Heidelberg)
서지주기
Includes bibliographical references.
일반주제명
Machine learning --Congresses.
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008 910208s1991 gw a b 100 0 eng
010 ▼a 91009131 //r92
020 ▼a 354053816X (Springer-Verlag Berlin Heidelberg New York)
020 ▼a 038753816X (Springer-Verlag New York Berlin Heidelberg)
040 ▼a DLC ▼c DLC
049 1 ▼l 121001606 ▼f 과학
050 0 0 ▼a Q325.5 ▼b .E87 1991
082 0 0 ▼a 006.3/1 ▼2 20
090 ▼a 006.31 ▼b E89m
111 2 ▼a European Working Session on Learning ▼d (1991 : ▼c Porto, Portugal)
245 1 0 ▼a Machine learning--EWSL-91 : ▼b proceedings / ▼c European Working Session on Learning, Porto, Portugal, March 6-8, 1991 ; Y. Kodratoff (ed.).
260 ▼a Berlin ; ▼a New York : ▼b Springer-Verlag, ▼c c1991.
300 ▼a xi, 537 p. : ▼b ill. ; ▼c 25 cm.
490 1 ▼a Lecture notes in computer science ; ▼v 482. ▼a Lecture notes in artificial intelligence.
504 ▼a Includes bibliographical references.
650 0 ▼a Machine learning ▼x Congresses.
700 1 ▼a Kodratoff, Yves.
830 0 ▼a Lecture notes in computer science ; ▼v 482.
830 0 ▼a Lecture notes in computer science. ▼p Lecture notes in artificial intelligence.

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.31 E89m 등록번호 121001606 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 세종학술정보원/과학기술실/ 청구기호 006.31 E89m 등록번호 151011589 도서상태 대출가능 반납예정일 예약 서비스
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.31 E89m 등록번호 121001606 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 세종학술정보원/과학기술실/ 청구기호 006.31 E89m 등록번호 151011589 도서상태 대출가능 반납예정일 예약 서비스

컨텐츠정보

목차

Abstracting background knowledge for concept learning.- A multistrategy learning approach to domain modeling and knowledge acquisition.- Using plausible explanations to bias empirical generalization in weak theory domains.- The replication problem: A constructive induction approach.- Integrating an explanation-based learning mechanism into a general problem-solver.- Analytical negative generalization and empirical negative generalization are not cumulative: A case study.- Evaluating and changing representation in concept acquisition.- Application of empirical discovery in knowledge acquisition.- Using accuracy in scientific discovery.- KBG : A generator of knowledge bases.- On estimating probabilities in tree pruning.- Rule induction with CN2: Some recent improvements.- On changing continuous attributes into ordered discrete attributes.- A method for inductive cost optimization.- When does overfitting decrease prediction accuracy in induced decision trees and rule sets?.- Semi-naive bayesian classifier.- Description contrasting in incremental concept formation.- System FLORA: Learning from time-varying training sets.- Message-based bucket brigade: An algorithm for the apportionment of credit problem.- Acquiring object-knowledge for learning systems.- Learning nonrecursive definitions of relations with linus.- Extending explanation-based generalization by abstraction operators.- Static learning for an adaptative theorem prover.- Explanation-based generalization and constraint propagation with interval labels.- Learning by explanation of failures.- PANEL : Logic and learnability.- Panel on : Causality and learning.- Seed space and version space: Generalizing from approximations.- Integrating EBL with automatic text analysis.- Abduction for explanation-based learning.- Consistent term mappings, term partitions, and inverse resolution.- Learning by analogical replay in prodigy: First results.- Analogical reasoning for logic programming.- Case-based learning of strategic knowledge.- Learning in distributed systems and multi-agent environments.- Learning to relate terms in a multiple agent environment.- Extending learning to multiple agents: Issues and a model for multi-agent machine learning (MA-ML).- Applications of machine learning: Notes from the panel members.- Evaluation of learning systems : An artificial data-based approach.- Shift of bias in learning from drug compounds: The fleming project.- Learning features by experimentation in chess.- Representation and induction of musical structures for computer assisted composition.- IPSA: Inductive protein structure analysis.- Four stances on knowledge acquisition and machine learning.- Programme of EWSL-91.


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