Algorithmic Learning Theory - ALT '92: Third Workshop, ALT '92, Tokyo, Japan, October 20-22, 1992. Proceedings: Lecture Notes in Computer Science, cartea 743
Editat de Shuji Doshita, Koichi Furukawa, Klaus P. Jantke, Toyaki Nishidaen Limba Engleză Paperback – 20 oct 1993
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Specificații
ISBN-13: 9783540573692
ISBN-10: 3540573690
Pagini: 276
Ilustrații: XII, 264 p.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.39 kg
Ediția:1993
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540573690
Pagini: 276
Ilustrații: XII, 264 p.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.39 kg
Ediția:1993
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
Discovery learning in intelligent tutoring systems.- From inductive inference to algorithmic learning theory.- A stochastic approach to genetic information processing.- On learning systolic languages.- A note on the query complexity of learning DFA.- Polynomial-time MAT learning of multilinear logic programs.- Iterative weighted least squares algorithms for neural networks classifiers.- Domains of attraction in autoassociative memory networks for character pattern recognition.- Regularization learning of neural networks for generalization.- Competitive learning by entropy minimization.- Inductive inference with bounded mind changes.- Efficient inductive inference of primitive Prologs from positive data.- Monotonic language learning.- Prudence in vacillatory language identification (Extended abstract).- Implementation of heuristic problem solving process including analogical reasoning.- Planning with abstraction based on partial predicate mappings.- Learning k-term monotone Boolean formulae.- Some improved sample complexity bounds in the probabilistic PAC learning model.- An application of Bernstein polynomials in PAC model.- On PAC learnability of functional dependencies.- Protein secondary structure prediction based on stochastic-rule learning.- Notes on the PAC learning of geometric concepts with additional information.