Titlebook: Learning Theory; 17th Annual Conferen John Shawe-Taylor,Yoram Singer Conference proceedings 2004 Springer-Verlag Berlin Heidelberg 2004 Boo

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Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversarytive adversary. In this problem we are given a bounded set . ⊆ ℝ. of feasible points. At each time step ., the online algorithm must select a point .. ∈ . while simultaneously an adversary selects a cost vector .. ∈ ℝ.. The algorithm then incurs cost ...... Kalai and Vempala show that even if . is e

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Learning Classes of Probabilistic Automataopen field of research. We show that PFA are identifiable in the limit with probability one. Multiplicity automata (MA) is another device to represent stochastic languages. We show that a MA may generate a stochastic language that cannot be generated by a PFA, but we show also that it is undecidable

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conception

Replacing Limit Learners with Equally Powerful One-Shot Query Learners change its mind arbitrarily often before converging to a correct hypothesis—to .—interpreting learning as a . in which the learner is required to identify the target concept with just one hypothesis. Although these two approaches seem rather unrelated at first glance, we provide characterizations o

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Learning a Hidden Graph Using ,(log ,) Queries Per Edgees an edge of the hidden graph. This model has been studied for particular classes of graphs by Kucherov and Grebinski [1] and Alon .[2], motivated by problems arising in genome sequencing. We give an adaptive deterministic algorithm that learns a general graph with . vertices and . edges using .(.

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Toward Attribute Efficient Learning of Decision Lists and Parities algorithm for learning decision lists of length . over . variables using 2. examples and time .. This is the first algorithm for learning decision lists that has both subexponential sample complexity and subexponential running time in the relevant parameters. Our approach is based on a new construc

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Learning Over Compact Metric Spacesipschitz functions on ., the Representer Theorem is derived. We obtain exact solutions in the case of least square minimization and regularization and suggest an approximate solution for the Lipschitz classifier.

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Local Complexities for Empirical Risk Minimization coordinate projections and show that this leads to a sharper error bound than the best previously known. The quantity which governs this bound on the empirical minimizer is the largest fixed point of the function .. We prove that this is the best estimate one can obtain using “structural results”,