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: Lecture Notes in Computer Science, cartea 872
Editat de Setsuo Arikawa, Klaus P. Jantkeen Limba Engleză Paperback – 28 sep 1994
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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Specificații
ISBN-13: 9783540585206
ISBN-10: 3540585206
Pagini: 596
Ilustrații: XV, 581 p.
Dimensiuni: 155 x 233 x 31 mm
Greutate: 0.83 kg
Ediția:1994
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540585206
Pagini: 596
Ilustrații: XV, 581 p.
Dimensiuni: 155 x 233 x 31 mm
Greutate: 0.83 kg
Ediția:1994
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
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 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 monotonicstrategies 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.