By Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles

ISBN-10: 3319116614

ISBN-13: 9783319116617

ISBN-10: 3319116622

ISBN-13: 9783319116624

This e-book constitutes the lawsuits of the twenty fifth overseas convention on Algorithmic studying thought, ALT 2014, held in Bled, Slovenia, in October 2014, and co-located with the seventeenth foreign convention on Discovery technological know-how, DS 2014. The 21 papers offered during this quantity have been rigorously reviewed and chosen from 50 submissions. moreover the e-book comprises four complete papers summarizing the invited talks. The papers are geared up in topical sections named: inductive inference; targeted studying from queries; reinforcement studying; on-line studying and studying with bandit details; statistical studying concept; privateness, clustering, MDL, and Kolmogorov complexity.

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Additional resources for Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings

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Ailon et al. [47] propose various methodologies to reduce the utility-based PB-MAB problem to the standard value-based MAB problem. In their setup, the utility of an arm is assumed to be in [0, 1]. Formally, u : S → [0, 1], and the link function is a linear function σlin (x) = 12 x. Therefore, the probability of an arm a ∈ S beating another arm a ∈ S is P(a a)= 1 + u(a) − u(a ) , 2 which is again in [0, 1]. The regret considered is the one defined in (2), where the reference arm ai∗ is the globally best arm with maximal utility.

Each decision to split requires on average at most 2 independent bits. The selection of a random direction to cut requires no more than 1 + log2 d independent bits. Since the total tree size is, with probability tending to 1, β+ε O(2log n ) for any ε > 0, a fact that follows with a bit of work from summing the expected number of nodes at each level, the total number of bits required to carry out all computations is O (3 + log2 d)2log β+ε n , which is orders of magnitude smaller than n provided that β + ε < 1.

An algorithm for constructing optimal binary decision trees. : A method for the design of binary tree classifiers. : Induction of decision trees. 5: Programs for Machine Learning. Machine Learning. : Data Mining with Decision Trees: Theory and Applications. : The quadtree and related hierarchical data structures. : The Design and Analysis of Spatial Data Structures. : Efficient decision tree design for discrete variable pattern recognition problems. : Multiple binary decision tree classifiers. : The Vapnik-Chervonenkis dimension of decision trees with bounded rank.

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Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings by Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles

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