Titlebook: Learning Theory; 18th Annual Conferen Peter Auer,Ron Meir Conference proceedings 2005 Springer-Verlag Berlin Heidelberg 2005 Boosting.Suppo

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Competitive Collaborative Learning.We formulate such learning tasks as an algorithmic problem based on the multi-armed bandit problem, but with a set of users (as opposed to a single user), of whom a constant fraction are honest and are partitioned into coalitions such that the users in a coalition perceive the same expected quality

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Analysis of Perceptron-Based Active Learning.. We then present a simple selective sampling algorithm for this problem, which combines a modification of the perceptron update with an adaptive filtering rule for deciding which points to query. For data distributed uniformly over the unit sphere, we show that our algorithm reaches generalization

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A New Perspective on an Old Perceptron Algorithmller than a predefined value. We derive worst case mistake bounds for our algorithm. As a byproduct we obtain a new mistake bound for the Perceptron algorithm in the inseparable case. We describe a multiclass extension of the algorithm. This extension is used in an experimental evaluation in which w

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Learnability of Bipartite Ranking Functionsss of ranking functions . that we term the rank dimension of ., and show that . is learnable only if its rank dimension is finite. Finally, we investigate questions of the computational complexity of learning ranking functions.

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A PAC-Style Model for Learning from Labeled and Unlabeled Datas one to estimate compatibility over the space of hypotheses, and reduce the size of the search space to those that, according to one’s assumptions, are a-priori reasonable with respect to the distribution. We discuss a number of technical issues that arise in this context, and provide sample-comple

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Generalization Error Bounds Using Unlabeled Dataor classifiers learned based on all the labeled data. The bound is easy to implement and apply and should be tight whenever cross-validation makes sense. Applying the bound to SVMs on the MNIST benchmark data set gives results that suggest that the bound may be tight enough to be useful in practice.

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Conference proceedings 2005ear was exceptionally high. In addition to the classical COLT topics, we found an increase in the number of submissions related to novel classi?cation scenarios such as ranking. This - crease re?ects a healthy shift towards more structured classi?cation problems, which are becoming increasingly rele

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András György,Tamás Linder,Gábor Lugosi for business system design, with a focus on performance management, motivation modeling, and communication; includes review questions and exercises at the endof each chapter..978-3-319-79213-2978-3-319-15102-1Series ISSN 2195-2817 Series E-ISSN 2195-2825