應(yīng)yl7703永利官網(wǎng)李朋副教授邀請, 浙江師范大學(xué)數(shù)學(xué)與計 算機(jī)科學(xué)學(xué)院向道紅教授, 將于2022年11月25號(星期五)下午15:00在線舉辦學(xué)術(shù)報告.

報告題目：Quantitative Convergence Analysis of Outcome Weighted Learning

騰訊會議號：718-342-643

報告摘要:The goal of precision medicine is to determine the optimal individualized treatment rules by considering the heterogeneity of patients, so as to maximize the expected clinical outcome. Outcome weighted learning (OWL) is one of the algorithms to estimate the optimal individualized treatment rules. In this talk we mainly study the convergence theory of OWL associated with varying Gaussians and general convex loss. Fisher consistency of OWL with convex loss is proved by making full use of the convexity of the loss function. Under some noise condition on distributions, quantitative relationship between weighted misclassification error and weighted generalization error is proved. The sample error is estimated by using a projection operator and a tight bound for the covering numbers of reproducing kernel Hilbert spaces generated by Gaussian kernels. Fast learning rates of OWL associated with least square loss, exponential-hinge loss and r-norm SVM loss are derived explicitly.

報告人簡介

向道紅,浙江師范大學(xué)數(shù)學(xué)與計算機(jī)科學(xué)學(xué)院教授,博士生導(dǎo)師,德國洪堡學(xué)者,浙江省高校中青年學(xué)科帶頭人,浙江省應(yīng)用數(shù)學(xué)研究會副理事長.于2009年2月獲香港城市大學(xué)博士學(xué)位, 2009-2010年在香港中文大學(xué)作博士后, 2010年3月入職浙江師范大學(xué)至今.研究領(lǐng)域為統(tǒng)計學(xué)習(xí)理論、穩(wěn)健統(tǒng)計等.在Journal of Machine Learning Research、Journal of Approximation Theory、Advances in Computational Mathematics、Journal of Multivariate Analysis、Science China Mathematics等國內(nèi)外學(xué)術(shù)刊物上發(fā)表論文多篇.主持完成國家自然科學(xué)基金面上項目2項,青年基金1項. 詳情見其主頁http://mypage.zjnu.edu.cn/XDH/zh_CN/index.htm

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萃英學(xué)院

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2022年11月17日