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Online pairwise learning algorithms with general convex loss functions without regularization in a Reproducing Kernel Hilbert Space (RKHS) are investigated. Under mild conditions on loss functions and the RKHS, upper bounds for the expected excess generalization error are derived in terms of the approximation error when the stepsize sequence decays polynomially. In particular, for Lipschitz loss functions...
Current studies on association rule mining focus on finding Boolean/quantitative association rules from certain databases or Boolean association rules from probabilistic databases. However, little work on mining association rules from probabilistic quantitative databases has been mentioned because the simultaneous measurement of quantitative information and probability is difficult. By introducing...
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