Algorithmic learning theory[electron...
Chaudhuri, Kamalika.

 

  • Algorithmic learning theory[electronic resource] :26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015 : proceedings /
  • 紀錄類型: 書目-語言資料,印刷品 : Monograph/item
    杜威分類號: 006.3
    書名/作者: Algorithmic learning theory : 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015 : proceedings // edited by Kamalika Chaudhuri, Claudio Gentile, Sandra Zilles.
    其他題名: ALT 2015
    其他作者: Chaudhuri, Kamalika.
    團體作者: Clark Conference
    出版者: Cham : : Springer International Publishing :, 2015.
    面頁冊數: xvii, 395 p. : : ill., digital ;; 24 cm.
    Contained By: Springer eBooks
    標題: Computer algorithms
    標題: Machine learning
    標題: Computer science.
    標題: Computers.
    標題: Data mining.
    標題: Artificial intelligence.
    標題: Pattern perception.
    標題: Computer Science.
    標題: Artificial Intelligence (incl. Robotics)
    標題: Theory of Computation.
    標題: Data Mining and Knowledge Discovery.
    標題: Pattern Recognition.
    ISBN: 9783319244860
    ISBN: 9783319244853
    內容註: Inductive inference -- Learning from queries, teaching complexity -- Computational learning theory and algorithms -- Statistical learning theory and sample complexity -- Online learning -- Stochastic optimization -- Kolmogorov complexity, algorithmic information theory.
    摘要、提要註: 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.
    電子資源: http://dx.doi.org/10.1007/978-3-319-24486-0
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