Among the topics addressed are PAC learning, learning algorithms, inductive learning, inductive inference, learning from examples, game-theoretical aspects, decision procedures, language learning, neural algorithms, and various other aspects of computational learning theory.

Program error detection/correction: Turning PAC learning into Perfect learning.- Team learning as a game.- Inferability of recursive real-valued functions.- Learning of R.E. Languages from good examples.- Identifiability of subspaces and homomorphic images of zero-reversible languages.- On exploiting knowledge and concept use in learning theory.- Partial occam's razor and its applications.- Derandomized learning of boolean functions.- Learning DFA from simple examples.- PAC learning under helpful distributions.- PAC learning using Nadaraya-Watson estimator based on orthonormal systems.- Monotone extensions of boolean data sets.- Classical Brouwer-Heyting-Kolmogorov interpretation.- Inferring a system from examples with time passage.- Polynomial time inductive inference of regular term tree languages from positive data.- Synthesizing noise-tolerant language learners.- Effects of Kolmogorov complexity present in inductive inference as well.- Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries.- Oracles in ? 2 p are sufficient for exact learning.- Exact learning via teaching assistants (Extended abstract).- An efficient exact learning algorithm for ordered binary decision diagrams.- Probability theory for the Brier game.- Learning and revising theories in noisy domains.- A note on a scale-sensitive dimension of linear bounded functionals in Banach Spaces.- On the relevance of time in neural computation and learning.- A simple algorithm for predicting nearly as well as the best pruning labeled with the best prediction values of a decision tree.- Learning disjunctions of features.- Learning simple deterministic finite-memory automata.- Learning acyclic first-order horn sentences from entailment.- On learning disjunctionsof zero-one threshold functions with queries.

GPSR Compliance The European Union's (EU) General Product Safety Regulation (GPSR) is a set of rules that requires consumer products to be safe and our obligations to ensure this. If you have any concerns about our products you can contact us on ProductSafety@springernature.com. In case Publisher is established outside the EU, the EU authorized representative is: Springer Nature Customer Service Center GmbH Europaplatz 3 69115 Heidelberg, Germany ProductSafety@springernature.com