Algorithmic Learning Theory : 4th 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. 1st ed. 1994

種類:

電子ブック

責任表示:

edited by Setsuo Arikawa, Klaus P. Jantke

出版情報:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1994

著者名:

• Arikawa, Setsuo.
• Jantke, Klaus P.
• SpringerLink (Online service)

シリーズ名:

Lecture Notes in Artificial Intelligence ; 872

ISBN:

9783540490302 [3540490302]    

注記:

Towards 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 digr
This 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.

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