By Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles
This publication constitutes the court cases of the twenty fifth overseas convention on Algorithmic studying conception, ALT 2014, held in Bled, Slovenia, in October 2014, and co-located with the seventeenth overseas convention on Discovery technological know-how, DS 2014. The 21 papers awarded during this quantity have been conscientiously reviewed and chosen from 50 submissions. moreover the ebook includes four complete papers summarizing the invited talks. The papers are equipped in topical sections named: inductive inference; distinct studying from queries; reinforcement studying; on-line studying and studying with bandit info; statistical studying concept; privateness, clustering, MDL, and Kolmogorov complexity.
Read Online or Download Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings PDF
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Additional resources for Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings
1 Axiomatic Approaches The seminal work of Yue et al.  relies on three regularity properties on the set of arms and their pairwise probabilities: – Total order over arms: there exists a total order on A, such that ai implies Δi,j > 0. – Strong stochastic transitivity: for any triplet of arms such that ai aj the pairwise probabilities satisfy Δi,k ≥ max (Δi,j , Δj,k ). – Stochastic triangle inequality: for any triplet of arms such that ai aj the pairwise probabilities satisfy Δi,k ≤ Δi,j + Δj,k .
Lecture Notes in Mathematics, vol. 757, pp. 5–22. : On tree structured classiﬁers. K. ) Artiﬁcial Neural Networks and Statistical Pattern Recognition, Old and New Connections, pp. 71–88. : An iterative growing and pruning algorithm for classiﬁcation tree design. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 163–174 (1991) 16 G. Biau and L. : Model selection for CART regression trees. : Asymptotically eﬃcient solutions to the classiﬁcation problem. : Classiﬁcation trees with neural network feature extraction.
An arm is then eliminated as soon as there is another arm with a signiﬁcantly higher score. In the regret analysis of BTM, a high probability bound is provided for a ﬁnite time horizon. More precisely, the regret accumulated by BTM is O γ7K log T minj=i∗ Δi∗ ,j with high probability. This result is stronger than the one proven for IF, in which only the expected regret is upper bounded. Moreover, this high probability regret bound matches with the expected regret bound in the case γ = 1 (strong stochastic transitivity).