jmlr jmlr2009 jmlr2009-68 jmlr2009-68-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Ting Hu, Ding-Xuan Zhou
Abstract: Learning algorithms are based on samples which are often drawn independently from an identical distribution (i.i.d.). In this paper we consider a different setting with samples drawn according to a non-identical sequence of probability distributions. Each time a sample is drawn from a different distribution. In this setting we investigate a fully online learning algorithm associated with a general convex loss function and a reproducing kernel Hilbert space (RKHS). Error analysis is conducted under the assumption that the sequence of marginal distributions converges polynomially in the dual of a H¨ lder space. For regression with least square or insensitive loss, learning rates are given o in both the RKHS norm and the L2 norm. For classification with hinge loss and support vector machine q-norm loss, rates are explicitly stated with respect to the excess misclassification error. Keywords: sampling with non-identical distributions, online learning, classification with a general convex loss, regression with insensitive loss and least square loss, reproducing kernel Hilbert space