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 foreign convention on Algorithmic studying idea, ALT 2013, held in Singapore in October 2013, and co-located with the sixteenth foreign convention on Discovery technology, DS 2013. The 23 papers provided during this quantity have been conscientiously reviewed and chosen from 39 submissions. furthermore the ebook includes three complete papers of invited talks. The papers are prepared in topical sections named: on-line studying, inductive inference and grammatical inference, instructing and studying from queries, bandit thought, statistical studying thought, Bayesian/stochastic studying, and unsupervised/semi-supervised learning.

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

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20 N. Ailon In the world of big data, pairwise preferences can be viewed as extremely sparse observations from latent distributions on rankings of massive sets. Instead of trying to define parametric models for these distributions (as described in Section 1), a modern non-parametric statistical approach should attempt to explain the data using a model that is, in some sense, simple. This approach mirrors recent work on matrix completion and sparse reconstruction, in which “model simplicity” is defined by various forms of sparsity.

Here the seed set S contains a subset family U of some ground set X such that u∈U u = X. The concept class for a seed U is φ(U ) = {c ∈ {0, 1}U | {u ∈ U | cu = 1} is a cover of X} . Truth Assignments to a CNF Formula: The online MAX-SAT problem is studied in [9]. The problem is specified by a set of disjunctive clauses (which is a seed) s = {C1 , C2 , . . , Cm } over k Boolean variables for some m and k, where each clause Ci is a disjunction of some literals. The online MAX-SAT problem for the seed s is the following: For each trial t, (i) the player predicts an assignment at ∈ {0, 1}k to the variables, (ii) the adversary returns weights t ∈ [0, 1]m for the clauses, and (iii) the player gets reward defined by the sum of weights t,i over those clauses Ci that are satisfied by at .

LNCS, vol. 7074, pp. 534–543. : Online Rank Aggregation. In: Proceedings of 4th Asian Conference on Machine Learning, ACML 2012, pp. : Lectures on Polytopes. Graduate Texts in Mathematics, vol. 152. Springer (1995) Exact Learning from Membership Queries: Some Techniques, Results and New Directions Nader H. il Abstract. Given a black box that contains a function f : D → R from some class of functions C. The black box can receive an element d (query) of the domain D and in time T returns the value f (d) ∈ R.

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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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