Algorithmic learning theory : 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001 : proceedings International Workshop on Algorithmic Learning Theory(著/文) - Springer 【利用不可】
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Algorithmic learning theory : 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001 : proceedings
- 初版年月日
- c2001-01
- 登録日
- 2016年4月18日
- 最終更新日
- 2016年4月18日
紹介
This book constitutes the refereed proceedings of the 12th International Conference on Algorithmic Learning Theory, ALT 2001, held in Washington, DC, USA in November 2001. The 21 revised full papers presented together with two invited papers and an introduction by the volume editors were carefully reviewed and selected from 42 submissions. The papers are organized in topical sections on complexity of learning, support vector machines, new learning models, online learning, inductive inference, refutable inductive inference, learning structures and languages.
目次
Editors' Introduction.- Editors' Introduction.- Invited Papers.- The Discovery Science Project in Japan.- Queries Revisited.- Robot Baby 2001.- Discovering Mechanisms: A Computational Philosophy of Science Perspective.- Inventing Discovery Tools: Combining Information Visualization with Data Mining.- Complexity of Learning.- On Learning Correlated Boolean Functions Using Statistical Queries (Extended Abstract).- A Simpler Analysis of the Multi-way Branching Decision Tree Boosting Algorithm.- Minimizing the Quadratic Training Error of a Sigmoid Neuron Is Hard.- Support Vector Machines.- Learning of Boolean Functions Using Support Vector Machines.- A Random Sampling Technique for Training Support Vector Machines.- New Learning Models.- Learning Coherent Concepts.- Learning Intermediate Concepts.- Real-Valued Multiple-Instance Learning with Queries.- Online Learning.- Loss Functions, Complexities, and the Legendre Transformation.- Non-linear Inequalities between Predictive and Kolmogorov Complexities.- Inductive Inference.- Learning by Switching Type of Information.- Learning How to Separate.- Learning Languages in a Union.- On the Comparison of Inductive Inference Criteria for Uniform Learning of Finite Classes.- Refutable Inductive Inference.- Refutable Language Learning with a Neighbor System.- Learning Recursive Functions Refutably.- Refuting Learning Revisited.- Learning Structures and Languages.- Efficient Learning of Semi-structured Data from Queries.- Extending Elementary Formal Systems.- Learning Regular Languages Using RFSA.- Inference of ?-Languages from Prefixes.
上記内容は本書刊行時のものです。
