José L. Balcázar, Philip M. Long, Frank Stephan's Algorithmic Learning Theory: 17th International Conference, PDF

By José L. Balcázar, Philip M. Long, Frank Stephan

ISBN-10: 3540466495

ISBN-13: 9783540466499

This publication constitutes the refereed complaints of the seventeenth foreign convention on Algorithmic studying conception, ALT 2006, held in Barcelona, Spain in October 2006, colocated with the ninth foreign convention on Discovery technological know-how, DS 2006.

The 24 revised complete papers awarded including the abstracts of 5 invited papers have been rigorously reviewed and chosen from fifty three submissions. The papers are devoted to the theoretical foundations of desktop studying. They deal with subject matters similar to question types, online studying, inductive inference, algorithmic forecasting, boosting, help vector machines, kernel equipment, reinforcement studying, and statistical studying models.

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Extra resources for Algorithmic Learning Theory: 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006. Proceedings

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ALT 2006, LNAI 4264, p. 28, 2006. c Springer-Verlag Berlin Heidelberg 2006 Reinforcement Learning and Apprenticeship Learning for Robotic Control Andrew Y. Ng Computer Science Department Stanford University Stanford CA 94304 Many robotic control problems, such as autonomous helicopter flight, legged robot locomotion, and autonomous driving, remain challenging even for modern reinforcement learning algorithms. ), (ii) It is often difficult to learn a good model of the robot’s dynamics, (iii) Even given a complete specification of the problem, it is often computationally difficult to find good closed-loop controller for a high-dimensional, stochastic, control task.

Fr (xr ) − f˜1 (x1 ) . . f˜r (xr )| ≤ |f1 (x1 ) . . fr (xr ) − f1 (x1 ) . . fr−1 (xr−1 )f˜r (xr )| + |f1 (x1 ) . . fr−1 (xr−1 )f˜r (xr ) − f˜1 (x1 ) . . f˜r (xr )| ≤ |fr (xr ) − f˜r (xr )| + |f˜r (xr )||f1 (x1 ) . . fr−1 (xr−1 ) − f˜1 (x1 ) . . ,xr−1 ) [|f1 (x1 ) . . fr−1 (xr−1 ) − f˜1 (x1 ) . . f˜r−1 (xr−1 )|]. xr xr =O( √1 ) k O(1) ≤1+ √ k We can repeat this argument successively until the base case Ex1 [|f1 (x1 ) − √ ; f˜1 (x1 )|] ≤ O( √1k ) is reached. Thus for some K = O(1), 1 < L = 1 + O(1) k r−1 O(1)r K i=0 Li √ √ E[|f1 (x1 ) .

Xi(Lmax −1) }. Let xi0 < xi1 < · · · < xit−1 and let τi : ZLmax → Li be the translation function such that τi (j) = xij . If Li = Li = {0} then τi is the function simply mapping 0 to 0. 6: Invoke GHS over f |S with accuracy /8. This is done by simulating MEM(f |S (x1 , . . , xn )) with MEM(f (τ1 (x1 ), τ2 (x2 ), . . , τn (xn ))). Let the output of the algorithm be g. 7: Let h be a hypothesis function over [b]n such that h(x1 , . . , xn ) = g(τ1−1 ( x1 ), . . , τn−1 ( xn )) ( xi denotes largest value in Li less than or equal to xi ).

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Algorithmic Learning Theory: 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006. Proceedings by José L. Balcázar, Philip M. Long, Frank Stephan


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