Algorithmic Learning Theory - ALT '92

Inhaltsverzeichnis zu „Algorithmic Learning Theory - ALT '92 “

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.