Learning Theory: 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings
This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA in June 2006. The 43 revised full papers presented together with 2 articles on open problems and 3 invited lectures were carefully reviewed and selected from a total of 102 submissions. The papers cover a wide range of topics including clustering, un- and semisupervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, learning algorithms and limitations on learning, online aggregation, online prediction and reinforcement learning.
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approximation assume assumption atleast Banach space Boolean function circuit classiﬁcation clustering coeﬃcients complexity Computer concept class consider constant constraints convergence convex convex hull convex set Corollary counterexamples deﬁned deﬁnition denote diﬀerent distribution dual eﬃcient eigenvalues entropy equivalence queries error estimation examples exists expected exponential ﬁnd ﬁnite ﬁrst ﬁxed function class function f Gaussians graph H.U. Simon Eds Hilbert space hypothesis implies inequality input iterative kernel label learnable learner learning algorithm Lemma linear loss function lower bound Lugosi and H.U. M. K. Warmuth Machine Learning matrix minimization noise norm obtain online algorithm optimal oracle parameter Perceptron polynomial positive prediction probability problem prove random regression sample satisfies sequence space speciﬁc spectral clustering stability statistical stochastic languages subset subspace target teaching dimension term transductive uniform update upper bound variables vector Warmuth weights