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学术报告:A magic cross validation theory for large margin classifiers

报告题目A magic cross validation theory for large margin classifiers



告嘉宾Professor Hui ZouUniversity of Minnesota



Cross validation is the most commonly used technique in machine learning for tuning the learning algorithm in order to achieve better generalization error rate. In this talk we present a magic CV theory which can allow users to very efficiently tune the support vector machine and related algorithms. The theory also provides a straightforward way to prove the Bayes consistency of these algorithms. We demonstrate our method on extensive simulations and benchmark data studies.



Zou Hui教授是明尼苏达大学统计系教授,国际数理统计学会会士(IMS Fellow)Zou Hui 教授于2011年获得IMS Tweedie Award以及2013年获得COGS Outstanding Faculty AwardZou Hui 教授曾任或现任统计学顶级期刊Journal of the Royal Statistical Society Series BAnnals of Statistics以及Journal of the American Statistical Association(与Biometrika合称统计学四大天王期刊)的Associate Editor,并任机器学习顶级期刊Journal of Machine Learning ResearchAction Editor

Zou Hui教授自2005年至今已在统计学四大天王期刊发表近30篇论文。Zou Hui教授的工作当选2006年“Fast Breaking Paper in Mathematics”以及2008年“New Hot Paper in Mathematics”;Zou Hui教授于2014-2017年均被评为ISI高被引科学家(ISI Highly Cited Researcher)。截至目前,Zou Hui教授所发表的论文合计被引用次数高达19154次;特别,Zou Hui教授和Hastie教授2005年合作提出的Elastic Net2006Zou Hui教授提出的Adaptive LASSO方法,和Hastie教授、Tibshirani教授合作提出的稀疏主成分分析方法被引用次数分别高达7633次、4045次和1966次。Zou Hui教授也是最早在国内参与提出统计优化学科的专家之一。

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