WebAug 17, 2007 · Title: Fast learning rates for plug-in classifiers. ... {-1}$, and (ii) the plug-in classifiers generally converge more slowly than the classifiers based on empirical risk … WebAug 19, 2024 · Theory 42 (1996) 2118--2132] to construct learning algorithms based on greedy approximations which are universally consistent and provide provable convergence rates for large classes of functions.
CiteSeerX — Fast learning rates for plug-in classifiers
WebJul 8, 2005 · Fast 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 classiflers attaining fast rates of convergence of the excess Bayes risk, i.e., the rates faster than n i1=2 . The works on this subject suggested the following two conjectures: (i) the ... WebOct 1, 2024 · The fast learning rate for sub-gaussian and sub-exponential losses are done in the context of density estimation , and for general losses , of which ... Fast learning rates for plug-in classifiers. Ann. Stat., 35 (2) (2007), pp. 608-633. View Record in Scopus Google Scholar. christofle chile
Learning From Non-iid Data: Fast Rates for the One-vs-All …
WebAug 11, 2024 · We enter the learning rates using the slice() function. Choosing a good learning rate seems to be more of an art than science and the Fastai course helps you learn the rules of thumb. Now that we … WebOct 23, 2024 · I am currently reading the paper Fast learning rates for plug-in classifiers under the margin condition by Audibert and Tsybakov (2014), in which the authors prove … WebJul 8, 2005 · 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 … get the best deals on motherboard