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020 _a9783540490302
_9978-3-540-49030-2
024 7 _a10.1007/3-540-58520-6
_2doi
050 4 _aQA8.9-QA10.3
072 7 _aUYA
_2bicssc
072 7 _aMAT018000
_2bisacsh
072 7 _aCOM051010
_2bisacsh
082 0 4 _a005.131
_223
245 1 0 _aAlgorithmic Learning Theory
_h[electronic resource] :
_b4th International Workshop on Analogical and Inductive Inference, AII '94 5th International Workshop on Algorithmic Learning Theory, ALT '94 Reinhardsbrunn Castle, Germany October 10–15, 1994 Proceedings /
_cedited by Setsuo Arikawa, Klaus P. Jantke.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c1994.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c1994.
300 _aXV, 581 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 ;
_v872
505 0 _aTowards efficient inductive synthesis from input/output examples -- Deductive plan generation -- From specifications to programs: Induction in the service of synthesis -- Average case analysis of pattern language learning algorithms -- Enumerable classes of total recursive functions: Complexity of inductive inference -- Derived sets and inductive inference -- Therapy plan generation as program synthesis -- A calculus for logical clustering -- Learning with higher order additional information -- Efficient learning of regular expressions from good examples -- Identifying nearly minimal Gödel numbers from additional information -- Co-learnability and FIN-identifiability of enumerable classes of total recursive functions -- On case-based represent ability and learnability of languages -- Rule-generating abduction for recursive prolog -- Fuzzy analogy based reasoning and classification of fuzzy analogies -- Explanation-based reuse of prolog programs -- Constructive induction for recursive programs -- Training digraphs -- Towards realistic theories of learning -- A unified approach to inductive logic and case-based reasoning -- Three decades of team learning -- On-line learning with malicious noise and the closure algorithm -- Learnability with restricted focus of attention guarantees noise-tolerance -- Efficient algorithm for learning simple regular expressions from noisy examples -- A note on learning DNF formulas using equivalence and incomplete membership queries -- Identifying regular languages over partially-commutative monoids -- Classification using information -- Learning from examples with typed equational programming -- Finding tree patterns consistent with positive and negative examples using queries -- Program synthesis in the presence of infinite number of inaccuracies -- On monotonic strategies for learning r.e. languages -- Language learning under various types of constraint combinations -- Synthesis algorithm for recursive processes by ?-calculus -- Monotonicity versus efficiency for learning languages from texts -- Learning concatenations of locally testable languages from positive data -- Language learning from good examples -- Machine discovery in the presence of incomplete or ambiguous data -- Set-driven and rearrangement-independent learning of recursive languages -- Refutably probably approximately correct learning -- Inductive inference of an approximate concept from positive data -- Efficient distribution-free population learning of simple concepts -- Constructing predicate mappings for Goal-Dependent Abstraction -- Learning languages by collecting cases and tuning parameters -- Mutual information gaining algorithm and its relation to PAC-learning algorithm -- Inductive inference of monogenic pure context-free languages.
520 _aThis volume presents the proceedings of the Fourth International Workshop on Analogical and Inductive Inference (AII '94) and the Fifth International Workshop on Algorithmic Learning Theory (ALT '94), held jointly at Reinhardsbrunn Castle, Germany in October 1994. (In future the AII and ALT workshops will be amalgamated and held under the single title of Algorithmic Learning Theory.) The book contains revised versions of 45 papers on all current aspects of computational learning theory; in particular, algorithmic learning, machine learning, analogical inference, inductive logic, case-based reasoning, and formal language learning are addressed.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aMathematical Logic and Formal Languages.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aArikawa, Setsuo.
_eeditor.
700 1 _aJantke, Klaus P.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540585206
786 _dSpringer
830 0 _aLecture Notes in Computer Science, Lecture Notes in Artificial Intelligence,
_x0302-9743 ;
_v872
856 4 0 _uhttp://dx.doi.org/10.1007/3-540-58520-6
942 _2EBK6641
_cEBK
999 _c35935
_d35935