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澳門皇冠官網(六):Pareto-based Multi-objective Machine Learning

發布日期:2018-09-27 作者:信息科學與工程學院

Abstract: This talk discusses the Pareto based approach to solving various machine learning problems. Although machine learning problems inherently have multiple objectives to optimize, these objectives are usually aggregated into a scalar objective optimization function so that traditional mathematical programing techniques such as the gradient based method can be applied. In this talk, we present ideas for solving a range of machine learning problems using evolutionary multi-objective optimization, including model selection and regularization, rule extraction, clustering, feature selection and ensemble generation. We suggest that the multi-objective approach to machine learning may create new perspectives in machine learning and opens up a new avenue for solving machine learning problems.

金耀初 (Yaochu Jin) 分別于198819911996年在浙江大學電機系獲學士、碩士及博士學位,并于2001年在德國波鴻魯爾大學獲工程博士學位。目前為英國薩里大學計算科學系計算智能首席教授,自然計算與應用研究組主任,薩里大學數學與計算生物學中心共同負責人。金耀初是長江學者獎勵計劃講座教授,芬蘭國家技術創新局芬蘭講座教授,IEEE Fellow目前擔任IEEE Transactions on Cognitive and Developmental Systems主編,Complex & Intelligent Systems主編。曾任IEEE計算智能學會副主席,Distinguished Lecturers Program 杰出講師。主要研究領域為進化優化,認知與發育系統,生物信息學。

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