Titlebook: Learning Theory; 20th Annual Conferen Nader H. Bshouty,Claudio Gentile Conference proceedings 2007 Springer-Verlag Berlin Heidelberg 2007 A

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書目名稱Learning Theory影響因子(影響力)




書目名稱Learning Theory影響因子(影響力)學科排名




書目名稱Learning Theory網絡公開度




書目名稱Learning Theory網絡公開度學科排名




書目名稱Learning Theory被引頻次




書目名稱Learning Theory被引頻次學科排名




書目名稱Learning Theory年度引用




書目名稱Learning Theory年度引用學科排名




書目名稱Learning Theory讀者反饋




書目名稱Learning Theory讀者反饋學科排名

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Minimax Bounds for Active Learningion error convergence for broad classes of distributions characterized by decision boundary regularity and noise conditions. The results clearly indicate the conditions under which one can expect significant gains through active learning. Furthermore we show that the learning rates derived are tight

Tartar

Stability of ,-Means Clusteringde variety of clustering applications. We continue the theoretical analysis of clustering stability by establishing a complete characterization of clustering stability in terms of the number of optimal solutions to the clustering optimization problem. Our results complement earlier work of Ben-David

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Learning Large-Alphabet and Analog Circuits with Value Injection Queriesor the class of transitively reduced circuits, we develop the Distinguishing Paths Algorithm, that learns such a circuit using (.). value injection queries and time polynomial in the number of queries. We describe a generalization of the algorithm to the class of circuits with shortcut width bounded

Metamorphosis

Teaching Dimension and the Complexity of Active Learningith membership queries, we derive upper and lower bounds on the label complexity in terms of generalizations of .. Among the contributions of this work is the first nontrivial general upper bound on label complexity in the presence of persistent classification noise.

混合,攙雜

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Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vectorn-asymptotic control of the confidence level. The random vector is supposed to be either Gaussian or to have a symmetric bounded distribution. We consider two approaches, the first based on a concentration principle and the second on a direct boostrapped quantile. The first one allows us to deal wit

吹牛者

Suboptimality of Penalized Empirical Risk Minimization in Classificationt possible rate the best procedure in .. This fastest rate is called optimal rate of aggregation. Considering a continuous scale of loss functions with various types of convexity, we prove that optimal rates of aggregation can be either ((log.)/.). or (log.)/.. We prove that, if all the . classifier