By Katharina Morik (auth.), Osamu Watanabe, Takashi Yokomori (eds.)

ISBN-10: 3540467696

ISBN-13: 9783540467694

ISBN-10: 3540667482

ISBN-13: 9783540667483

This publication constitutes the refereed complaints of the tenth foreign convention on Algorithmic studying thought, ALT'99, held in Tokyo, Japan, in December 1999.
The 26 complete papers offered have been rigorously reviewed and chosen from a complete of fifty one submissions. additionally integrated are 3 invited papers. The papers are equipped in sections on studying measurement, Inductive Inference, Inductive good judgment Programming, PAC studying, Mathematical instruments for studying, studying Recursive services, question studying and online studying.

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What size net gives valid generalization? Neural Computation, 1(1):151–160, 1989. [5] Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, pages 144–152, 1992. [6] Leo Breiman. Arcing the edge. Technical Report 486, Statistics Department, University of California at Berkeley, 1997. [7] Leo Breiman. Prediction games and arcing classifiers. Technical Report 504, Statistics Department, University of California at Berkeley, 1997.

Direct optimization of margins improves generalization in combined classifiers. Technical report, Deparment of Systems Engineering, Australian National University, 1998. [33] C. J. Merz and P. M. Murphy. UCI repository of machine learning databases, 1999. html. [34] J. R. Quinlan. 5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 725–730, 1996. [35] J. Ross Quinlan. 5: Programs for Machine Learning. Morgan Kaufmann, 1993. [36] Robert E. Schapire. The strength of weak learnability.

T=1 ˜ k = {fθ (·) : θ ∈ ∆} and A be the aggregating algorithm AG using Letting H ˜ Hk , by Lemma 1, we see m ˜k) L(yt , yˆt ) ≤ I(Dm : H t=1 =− 1 1 ln λ∗ N ∗ e−λ t=1 L(yt ,fθ (xt )) θ∈∆ m ≤ m L(yt , fθ¯(xt )) + t=1 1 ln N. λ∗ This leads: m m L(yt , yˆt ) − sup Dm ∈D m (Θ) t=1 L(yt , fθ¯(xt )) t=1 ≤ 1 ln N. λ∗ (18) By Taylor expansion argument, for all Dm ∈ Dm (Θ), m m L(yt , fθ¯(xt )) − t=1 L(yt , fθˆ(xt )) ≤ t=1 µm ˆ ¯ 2 µmF 2 k |θ − θ| ≤ 2 2 V N 2/k . (19) Plugging (18) and (19) into (17) yields sup Dm ∈D m (Θ) R(AG : Dm ) ≤ 1 µmF 2 k ln N + λ∗ 2 V N 2/k .

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Algorithmic Learning Theory: 10th International Conference, ALT’99 Tokyo, Japan, December 6–8, 1999 Proceedings by Katharina Morik (auth.), Osamu Watanabe, Takashi Yokomori (eds.)


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