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Titlebook: Algorithmic Learning Theory; 26th International C Kamalika Chaudhuri,CLAUDIO GENTILE,Sandra Zilles Conference proceedings 2015 Springer Int

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发表于 2025-3-21 16:57:40 | 显示全部楼层 |阅读模式
期刊全称Algorithmic Learning Theory
期刊简称26th International C
影响因子2023Kamalika Chaudhuri,CLAUDIO GENTILE,Sandra Zilles
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Algorithmic Learning Theory; 26th International C Kamalika Chaudhuri,CLAUDIO GENTILE,Sandra Zilles Conference proceedings 2015 Springer Int
影响因子.This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory..
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发表于 2025-3-21 20:42:33 | 显示全部楼层
Exact Learning of Multivalued Dependenciesgluin et al.’s exact learning model with membership and equivalence queries, provided that counterexamples and membership queries are formulated as 2-Quasi-Horn clauses. As a consequence, we obtain that the subclass of 2-Quasi-Horn theories which are equivalent to MVDF is polynomially learnable.
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Learning with a Drifting Target ConceptWe also provide general results for an algorithm capable of adapting to a variable rate of drift of the target concept. Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the error rates.
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Interactive Clustering of Linear Classes and Cryptographic Lower Boundser .. We also give cryptographic hardness results for interactive clustering. In particular, we show that, under plausible cryptographic assumptions, the interactive clustering problem is intractable for the concept classes of polynomial-size constant-depth threshold circuits, Boolean formulas, and finite automata.
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Priced Learningry – depending on the current datum – and requiring that the overall sum of the costs incurred has to be finite. There are priced-learnable classes which are not iteratively learnable. The current work introduces the basic definitions and results for priced learning. This work also introduces various variants of priced learning.
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0302-9743 arning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory..978-3-319-24485-3978-3-319-24486-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
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