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

ISBN-10: 3319116614

ISBN-13: 9783319116617

ISBN-10: 3319116622

ISBN-13: 9783319116624

This ebook constitutes the lawsuits of the twenty fifth overseas convention on Algorithmic studying idea, 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 awarded during this quantity have been conscientiously reviewed and chosen from 50 submissions. additionally the booklet includes four complete papers summarizing the invited talks. The papers are prepared in topical sections named: inductive inference; targeted studying from queries; reinforcement studying; on-line studying and studying with bandit details; statistical studying thought; privateness, clustering, MDL, and Kolmogorov complexity.

**Read or Download Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings PDF**

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

**Example text**

Min Δi∗ ,i(t) , Δi∗ ,j(t) 25 From a theoretical point of view, when the number of pairwise comparisons is bounded by a known horizon, these regret deﬁnitions do not lead to a fundamentally diﬀerent problem. Roughly speaking, this is because most of the methods designed for optimizing regret seek to identify the best arm with high probability in the exploration phase, based on as few sample as possible. , the pairwise probabilities qi,j . The target of the agent’s prediction, however, is not the relation Q itself, but the best arm or, more generally, a ranking of all arms A.

More speciﬁcally, with P(r) the probability of observing the ranking r, the probability qi,j that ai is preferred to aj is obtained by summing over all rankings r in which ai precedes aj : qi,j = P(ai aj ) = P(r) (4) r∈L(rj >ri ) where L(rj > ri ) = {r ∈ SK | rj > ri } denotes the subset of permutations for which the rank rj of aj is higher than the rank ri of ai (smaller ranks indicate higher preference). In this setting, the learning problem essentially comes down to making inference about P based on samples in the form of pairwise comparisons.

A tree-structured approach to nonparametric multiple regression. , Rosenblatt, M. ) Smoothing Techniques for Curve Estimation. 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.

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