000 04567nam a22004935i 4500
001 978-3-540-46308-5
003 DE-He213
005 20160624102014.0
007 cr nn 008mamaa
008 121227s1991 gw | s |||| 0|eng d
020 _a9783540463085
_9978-3-540-46308-5
024 7 _a10.1007/BFb0016999
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aMachine Learning — EWSL-91
_h[electronic resource] :
_bEuropean Working Session on Learning Porto, Portugal, March 6–8, 1991 Proceedings /
_cedited by Yves Kodratoff.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c1991.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c1991.
300 _aXI, 541 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science, Lecture Notes in Artificial Intelligence,
_x0302-9743 ;
_v482
505 0 _aAbstracting 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.
650 0 _aComputer science.
650 0 _aSoftware engineering.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aSoftware Engineering.
700 1 _aKodratoff, Yves.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540538165
786 _dSpringer
830 0 _aLecture Notes in Computer Science, Lecture Notes in Artificial Intelligence,
_x0302-9743 ;
_v482
856 4 0 _uhttp://dx.doi.org/10.1007/BFb0016999
942 _2EBK5757
_cEBK
999 _c35051
_d35051