000 | 04567nam a22004935i 4500 | ||
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001 | 978-3-540-46308-5 | ||
003 | DE-He213 | ||
005 | 20160624102014.0 | ||
007 | cr nn 008mamaa | ||
008 | 121227s1991 gw | s |||| 0|eng d | ||
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_a9783540463085 _9978-3-540-46308-5 |
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024 | 7 |
_a10.1007/BFb0016999 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTJ210.2-211.495 | |
072 | 7 |
_aUYQ _2bicssc |
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_aTJFM1 _2bicssc |
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_aCOM004000 _2bisacsh |
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_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. |
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264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c1991. |
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300 |
_aXI, 541 p. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, _x0302-9743 ; _v482 |
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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. |
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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 |
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856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/BFb0016999 |
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_2EBK5757 _cEBK |
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999 |
_c35051 _d35051 |