Computational learning theory : EuroCOLT '93 : based on the proceedings of the First European Conference on Computational Learning Theory, organized by the Institute of Mathematics and Its Applications and held at Royal Holloway, University of London in December, 1993
John S. Shawe-Taylor, Martin Anthony · Oxford University Press eBooks · 1994
W. Maass: On the complexity of learning on neural nets M. Frazier and L. Pitt: Some new directions in computational learning theory L.G. Valiant: A neuroidal model for cognitive functions J. Kivinen, H. Mannila and E. Ukkonen: Learning rules with local exceptions M. Golea and M. Marchand: On learning simple deterministic and probabilistic neural concepts P. Fischer: Learning unions of convex polygons T. Hegedus: On training simple neural networks and small-weight neurons H.U. Simon: Bounds on the number of examples needed for learning functions M. Anthony and J. Shawe-Taylor: Valid generalization of functions from close approximations on a sample J. Kivinen and M.K. Warmuth: Using experts for predicting continuous outcomes K. Pillaipakkamnatt and V. Raghavan: Read-twice DNF formulas are properly learnable F. Ameur, P. Fischer, K.U. Hoeffgen and F. Meyer auf der Heide: Trial and error: a new approach to space-bounded learning S. Anoulova and S. Poelt: Using Kullback-Leibler divergence in learning theory Saoudi Yokomori: Learning local and recognizable w-languages and monadic logic programs R. Wiehagen, C.H. Smith and T. Zeugmann: Classification of predicates and languages H. Wiklicky: The neural network loading problem is undecidable R. Gavalda: On the power of equivalence On-line prediction and conversion strategies K. Yamanishi: Learning non-parametric smooth rules by stochastic rules with finite partitioning S. Poelt: Improved sample size bounds for PAB-decisions.