Fast Learning Rates for Plug-in Classifiers
نویسندگان
چکیده
It has been recently shown that, under the margin (or low noise) assumption, there exist classifiers attaining fast rates of convergence of the excess Bayes risk, i.e., the rates faster than n−1/2. The works on this subject suggested the following two conjectures: (i) the best achievable fast rate is of the order n−1, and (ii) the plug-in classifiers generally converge slower than the classifiers based on empirical risk minimization. We show that both conjectures are not correct. In particular, we construct plug-in classifiers that can achieve not only the fast, but also the super-fast rates, i.e., the rates faster than n−1. We establish minimax lower bounds showing that the obtained rates cannot be improved. http://www.enpc.fr/certis/Papers/05preprint_AudTsy.pdf
منابع مشابه
Fast Learning Rates for Plug-in Classifiers by Jean-yves Audibert
It has been recently shown that, under the margin (or low noise) assumption, there exist classifiers attaining fast rates of convergence of the excess Bayes risk, that is, rates faster than n−1/2. The work on this subject has suggested the following two conjectures: (i) the best achievable fast rate is of the order n−1, and (ii) the plug-in classifiers generally converge more slowly than the cl...
متن کاملFast learning rates for plug-in classifiers under the margin condition
It has been recently shown that, under the margin (or low noise) assumption, there exist classifiers attaining fast rates of convergence of the excess Bayes risk, i.e., the rates faster than n−1/2. The works on this subject suggested the following two conjectures: (i) the best achievable fast rate is of the order n−1, and (ii) the plug-in classifiers generally converge slower than the classifie...
متن کاملMinimax Learning Rates for Bipartite Ranking and Plug-in Rules
While it is now well-known in the standard binary classification setup, that, under suitable margin assumptions and complexity conditions on the regression function, fast or even super-fast rates (i.e. rates faster than n or even faster than n) can be achieved by plug-in classifiers, no result of this nature has been proved yet in the context of bipartite ranking, though akin to that of classif...
متن کاملA Hybrid Framework for Building an Efficient Incremental Intrusion Detection System
In this paper, a boosting-based incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As...
متن کاملOptimal Aggregation of Classifiers in Statistical Learning by Alexandre
Classification can be considered as nonparametric estimation of sets, where the risk is defined by means of a specific distance between sets associated with misclassification error. It is shown that the rates of convergence of classifiers depend on two parameters: the complexity of the class of candidate sets and the margin parameter. The dependence is explicitly given, indicating that optimal ...
متن کامل