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信息学院学术报告(六):Pareto-based Multi-objective Machine Learning

发布日期:2018-09-27 作者:信息科学与工程学院

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) 分别于198819911996年在浙江大学电机系获学士、硕士及博士学位,并于2001年在德国波鸿鲁尔大学获工程博士学位。目前为英国萨里大学计算科学系计算智能首席教授,自然计算与应用研究组主任,萨里大学数学与计算生物学中心共同负责人。金耀初是长江学者奖励计划讲座教授,芬兰国家技术创新局芬兰讲座教授,IEEE Fellow目前担任IEEE Transactions on Cognitive and Developmental Systems主编,Complex & Intelligent Systems主编。曾任IEEE计算智能学会副主席,Distinguished Lecturers Program 杰出讲师。主要研究领域为进化优化,认知与发育系统,生物信息学。