Algorithmic Learning Theory: 24th International Conference, by Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann PDF

By Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann

ISBN-10: 3642409342

ISBN-13: 9783642409349

ISBN-10: 3642409350

ISBN-13: 9783642409356

This ebook constitutes the complaints of the twenty fourth overseas convention on Algorithmic studying concept, ALT 2013, held in Singapore in October 2013, and co-located with the sixteenth overseas convention on Discovery technology, DS 2013. The 23 papers offered during this quantity have been conscientiously reviewed and chosen from 39 submissions. furthermore the ebook comprises three complete papers of invited talks. The papers are equipped in topical sections named: on-line studying, inductive inference and grammatical inference, instructing and studying from queries, bandit conception, statistical studying conception, Bayesian/stochastic studying, and unsupervised/semi-supervised learning.

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Extra resources for Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings

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One of the important representation of boolean functions f : {0, 1}n → {0, 1} is decision tree. A decision tree is defined as follows: The constant functions 0 and 1 are decision trees. If f0 and f1 are decision trees then “f ≡(if xi = 0 then f0 else f1 )” is a decision tree (can also be expressed as f = xi f1 + x ¯i f0 ). H. Bshouty a tree T (f ). If f ≡ 1 or 0 then T (f ) is a node labeled with 1 or 0, respectively. If f ≡(if xi = 0 then f0 else f1 ), then T (f ) has a root labeled with xi and has two outgoing edges.

Ordering by weighted number of wins gives a good ranking for weighted tournaments. ACM Trans. : Spearman’s footrule as a measure of disarray. : Rank aggregation methods for the web. In: Proceedings of the Tenth International Conference on the World Wide Web, WWW 2010, Hong Kong, pp. : Comparing and aggregating rankings with ties. In: Proceedings of the Twenty-Third ACM SIGMODSIGACT-SIGART Symposium on Principles of Database Systems, pp. : A latent pairwise preference learning approach for recommendation from implicit feedback.

Moreover, the Decomposition can also be computed by using Orlin’s SFM algorithm, in which the dual optimal solution is found in the form of convex combination of some extreme points. We summarize the results as in the following theorem. Theorem 5. Let Φ be a separable and strictly convex function over a strictly convex set Γ such that B(f ) ⊆ Γ . Then, there exist algorithms that solve the projection onto B(f ) with respect to ΔΦ and the decomposition for C in time O(n6 + n5 EO), where EO denotes the unit time to evaluate the value of the submodular function f .

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Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings by Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann


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