Algorithmic Learning Theory

Identifying and using patterns in sequential data.- Learning theory toward Genome Informatics.- Optimal layered learning: A PAC approach to incremental sampling.- Reformulation of explanation by linear logic toward logic for explanation.- Towards efficient inductive synthesis of expressions from input/output examples.- A typed ?-calculus for proving-by-example and bottom-up generalization procedure.- Case-based representation and learning of pattern languages.- Inductive resolution.- Generalized unification as background knowledge in learning logic programs.- Inductive inference machines that can refute hypothesis spaces.- On the duality between mechanistic learners and what it is they learn.- On aggregating teams of learning machines.- Learning with growing quality.- Use of reduction arguments in determining Popperian FIN-type learning capabilities.- Properties of language classes with finite elasticity.- Uniform characterizations of various kinds of language learning.- How to invent characterizable inference methods for regular languages.- Neural Discriminant Analysis.- A new algorithm for automatic configuration of Hidden Markov Models.- On the VC-dimension of depth four threshold circuits and the complexity of Boolean-valued functions.- On the sample complexity of consistent learning with one-sided error.- Complexity of computing Vapnik-Chervonenkis dimension.- ?-approximations of k-label spaces.- Exact learning of linear combinations of monotone terms from function value queries.- Thue systems and DNA — A learning algorithm for a subclass.- The VC-dimensions of finite automata with n states.- Unifying learning methods by colored digraphs.- A perceptual criterion for visually controlling learning.- Learning strategies using decision lists.- A decomposition based induction model for discovering concept clusters from databases.- Algebraic structure of some learning systems.- Induction of probabilistic rules based on rough set theory.