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Titlebook: Algorithmic Learning Theory - ALT ‘92; Third Workshop, ALT Shuji Doshita,Koichi Furukawa,Toyaki Nishida Conference proceedings 1993 Spring

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發(fā)表于 2025-3-25 07:13:53 | 只看該作者
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發(fā)表于 2025-3-25 07:45:55 | 只看該作者
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發(fā)表于 2025-3-25 14:51:54 | 只看該作者
Conference proceedings 1993de an open forum fordiscussions and exchanges of ideasbetween researchers fromvarious backgrounds in this emerging,interdisciplinaryfield of learning theory. The volume is organized intopartson learning via query, neural networks, inductive inference,analogical reasoning, and approximate learning.
24#
發(fā)表于 2025-3-25 15:50:35 | 只看該作者
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發(fā)表于 2025-3-25 22:11:13 | 只看該作者
Der Betonpfahl in Theorie und Praxist examples are drawn according to the uniform distribution. In this paper we generalize the result to obtain the statement that .-term MDNF is learnable even if positive examples are drawn according to such distribution that the maximum of the ratio of the probabilities of two positive examples is bounded from above by some polynomial.
26#
發(fā)表于 2025-3-26 01:17:07 | 只看該作者
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發(fā)表于 2025-3-26 08:52:03 | 只看該作者
Der Betafaktor in der Wissenschaft,em, common patterns (stochastic motifs) are represented by stochastic decision predicates, and a genetic algorithm with Rissanen‘s minimum description length principle is used to select “good stochastic motifs” from the viewpoint of increasing prediction performance.
29#
發(fā)表于 2025-3-26 12:51:29 | 只看該作者
30#
發(fā)表于 2025-3-26 18:46:56 | 只看該作者
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