Computational Learning Theory

The discovery of algorithmic probability: A guide for the programming of true creativity.- A desicion-theoretic generalization of on-line learning and an application to boosting.- Online learning versus offline learning.- Learning distributions by their density levels — A paradigm for learning without a teacher.- Tight worst-case loss bounds for predicting with expert advice.- On-line maximum likelihood prediction with respect to general loss functions.- The power of procrastination in inductive inference: How it depends on used ordinal notations.- Learnability of Kolmogorov-easy circuit expressions via queries.- Trading monotonicity demands versus mind changes.- Learning recursive functions from approximations.- On the intrinsic complexity of learning.- The structure of intrinsic complexity of learning.- Kolmogorov numberings and minimal identification.- Stochastic complexity in learning.- Function learning from interpolation (extended abstract).- Approximation and learning of convex superpositions.- Minimum description length estimators under the optimal coding scheme.- MDL learning of unions of simple pattern languages from positive examples.- A note on the use of probabilities by mechanical learners.- Characterizing rational versus exponential learning curves.- Is pocket algorithm optimal?.- Some theorems concerning the free energy of (Un) constrained stochastic Hopfield neural networks.- A space-bounded learning algorithm for axis-parallel rectangles.- Learning decision lists and trees with equivalence-queries.- Bounding VC-dimension for neural networks: Progress and prospects.- Average case analysis of a learning algorithm for ?-DNF expressions.- Learning by extended statistical queries and its relation to PAC learning.- Typed pattern languages and their learnability.- Learning behaviors of automata from shortest counterexamples.- Learning of regular expressions by pattern matching.- The query complexity of learning some subclasses of context-free grammars.