Ivan Bratko, Nada Lavrac (Eds.): Progress in Machine Learning, Proceedings of EWSL 87: 2nd European Working Session on Learning, Bled, Yugoslavia, May 1987. Sigma Press, Wilmslow, 1987, ISBN1-85058-088-X

Part One: Learning from Examples in the Presence of Noise

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  Peter Clark, Tim Niblett: Induction in Noisy Domains. 11-30
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  Bojan Cestnik, Igor Kononenko, Ivan Bratko: ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users. 31-45
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  Matjaz Gams, Nada Lavrac: Review of Five Empirical Learning Systems Within a Proposed Schemata. 46-66
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  Tim Niblett: Constructing Decision Trees in Noisy Domains. 67-78
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  Christopher J. C. H. Watkins: Combining Cross-Validation and Search. 79-87

Part Two: Meta-Knowledge and Explanation in Learning

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  Yves Kodratoff: Is AI a Sub-Field of Computer Science - or is AI the Science of Explanations? 91-106
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  Roberto Desimone: Learning Control Knowledge Within an Explanation-Based Learning Framework. 107-120
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  Jean-Francois Puget: Goal Regression with Opponent. 121-137
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  Pavel Brazdil: Knowledge States and Meta-Knowledge Maintenance. 138-146

Part Three: Studies of Special Mechanisms for Learning

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  Nicolas Helft: Inductive Generalization: A Logical Framework. 149-157
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  Jean-Gabriel Ganascia: Learning with Hilbert Cubes. 158-171
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  Jiarong Hong, Carl T. Uhrik: The Extension Matrix Approach to Attribute Based Learning. 172-182
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  Michel Manago, Yves Kodratoff: Model Driven Learning of Disjunctive Concepts. 183-198

Part Four: Learning in Complex Domains

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  Igor Mozetic: Learning of Qualitative Models. 201-217
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  Stephen Muggleton: Structuring Knowledge by Asking Questions. 218-229
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  Ross D. King: An Inductive Learning Approach to the Problem of Predicting a Protein's Secondary Structure from Its Amino Acid Sequence. 230-250