http://www.bibsonomy.org/publrss/user/schaul/of,BibSonomy RSS Feed for /user/schaul/of,idsiahttp://www.bibsonomy.org/bibtex/2a6f77b57f9dd6af428692b40ecc13a73/schaulschaul2008-02-26T11:58:58+01:00rate, and, general, bounds, hierarchy, leader, perturbed, alphabet, prediction, expected, learning, the, probability, advice, weights, online, high, experts, of, follow, adversary, expert, adaptive, sequential, loss, with juergen, <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Hutter">Hutter</a> und J. <a href="http://www.bibsonomy.org/author/Poland">Poland</a> </span><em>Journal of Machine Learning Research</em>(<em>2005</em>)idsiahttp://www.bibsonomy.org/bibtex/2b9394ee75dd5db37341b62745634de89/schaulschaul2008-02-26T11:58:58+01:00bounds, weights, follow, expert, learning, loss, leader, perturbed, advice, juergen, adaptive, prediction, expected, with sequential, hierarchy, the, rate, alphabet, online, probability, experts, of, high, and, general, <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Hutter">Hutter</a> und J. <a href="http://www.bibsonomy.org/author/Poland">Poland</a> </span><em>Proc. 15th International Conf. on Algorithmic Learning Theory (ALT'04), </em><em>Volume3244vonLNAI, </em><em>Seite279--293. </em><em>Padova, </em><em>Springer, Berlin, </em>(<em>2004</em>)idsiahttp://www.bibsonomy.org/bibtex/208a8dfde4e1c9fe9b6f6b1ebe570cece/schaulschaul2008-02-26T11:58:58+01:00selection, basic, problems, juergen, deceptive, optimization, principles, schemes, computations, multimodal, of, evaluation, evolutionary, landscapes, working fitness, selfadaptation, algorithm, <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Legg">Legg</a> und M. <a href="http://www.bibsonomy.org/author/Hutter">Hutter</a> und A. <a href="http://www.bibsonomy.org/author/Kumar">Kumar</a> </span><em>Proc. 2004 Congress on Evolutionary Computation (CEC'04), </em><em>Seite2144--2151. </em><em>Portland, OR, </em><em>IEEE, </em>(<em>2004</em>)idsiahttp://www.bibsonomy.org/bibtex/23e33239f0046b223f066a4fbe60edcd6/schaulschaul2008-02-26T11:58:58+01:00and, statistics, variance information, naive, order, distribution, bayesian, cross, of, mutual, juergen, second, expectation, selection, bayes, entropy, filters, classifier, feature, dirichlet, <span style="color:#555555;">Marcus <a href="http://www.bibsonomy.org/author/Hutter">Hutter</a> und Marco <a href="http://www.bibsonomy.org/author/Zaffalon">Zaffalon</a> </span><em>Computational Statistics \&amp; Data Analysis</em><em>48(3):633--657</em>(<em>2005</em>)idsiahttp://www.bibsonomy.org/bibtex/2f65403f6346a5b6747be35f3f4f3ac2f/schaulschaul2008-02-26T11:58:58+01:00credible, data, naive, intervals, entropy, selection, information, classifier, filter, expectation, cross, second, and, juergen, missing, of, bayes, order, statistics, feature, variance distribution, bayesian, robust, approach, dirichlet, mutual, <span style="color:#555555;">Marco <a href="http://www.bibsonomy.org/author/Zaffalon">Zaffalon</a> und Marcus <a href="http://www.bibsonomy.org/author/Hutter">Hutter</a> </span><em>Proc. 18th International Conf. on Uncertainty in Artificial Intelligence (UAI-2002), </em><em>Seite577--584. </em><em>Morgan Kaufmann, San Francisco, CA, </em>(<em>2002</em>)idsiahttp://www.bibsonomy.org/bibtex/2196353ab4f02ae1e2973c01d08d38c45/schaulschaul2008-02-26T11:58:58+01:00of, distribution, mutual, information, order, expectation, variance entropy, juergen, second, cross, and, dirichlet, <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Hutter">Hutter</a> </span><em>Advances in Neural Information Processing Systems 14, </em><em>Seite399--406. </em><em>Cambridge, MA, </em><em>MIT Press, </em>(<em>2002</em>)