Get Algorithmic Learning Theory: 24th International Conference, PDF

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

ISBN-10: 3642409342

ISBN-13: 9783642409349

ISBN-10: 3642409350

ISBN-13: 9783642409356

This e-book constitutes the lawsuits of the twenty fourth overseas convention on Algorithmic studying conception, 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 publication comprises three complete papers of invited talks. The papers are geared up in topical sections named: on-line studying, inductive inference and grammatical inference, educating and studying from queries, bandit concept, statistical studying thought, Bayesian/stochastic studying, and unsupervised/semi-supervised learning.

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Read Online or Download Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings PDF

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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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4 Using Offline Approximation Algorithms In this section, we give a short review for the result of [9] which appears in this proceedings. Let C be our concept class, and assume that we have a polynomial time αapproximation algorithm for OPT(C) by continuous relaxation. More precisely, we assume the following: Assumption 1 1. There is a relaxed space P with C ⊆ P. 2. There is a polynomial time algorithm A that finds c ∈ C such that c · ≤ α minx∈P x · for a given loss vector ∈ L. 3. There is an online linear optimization algorithm B with the decision space P such that it runs in polynomial time per trial and has regret bound of Reg B (T ), where T is the horizon of the game.

Perceptron, Threshold) A (boolean) Halfspace is a function f : {0, 1}n → {0, 1} of the following form: f (x1 , . . , xn ) = 1, 0, a1 x1 + a2 x2 + · · · + an xn ≥ b ; otherwise, where a1 , . . , an , b are real numbers. The size of f is the number of bits that are needed to represent the numbers a1 , . . , an , b. New Direction 1. There are three important classes that are not studied enough in the literature in the exact learning model: The class of Multiplicity Automata Function, CDNF and Halfspace.

It is originally proposed for a convex decision space rather than for a combinatorial concept class. Efficient Algorithms for Combinatorial Online Prediction 27 FTRL over a Convex Decision Space. We first consider the online linear optimization problem over a convex decision space P ⊆ Rn . Let Φ : Γ → R be a strictly convex function defined on a closed convex set Γ ⊆ Rn with P ⊆ Γ . Then, the Bregman divergence ΔΦ with respect to Φ is defined as ΔΦ (p, q) = Φ(p) − Φ(q) − ∇Φ(q) · (p − q). The function Φ is separable if there exists functions φi : Γi → R for i = 1, 2, .

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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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