英国萨里大学Yaochu Jin教授报告通知

承办单位：国际智能感知与计算研究中心

               “智能感知与图像理解”教育部重点实验室

                  国家111计划创新引智基地

                  IEEE西安分会

                  IET西安分会

报告地点：主楼Ⅱ-448报告厅

报告时间：2014年7月17日 10:00a.m.

报告题目：Pareto-Based Multiobjective Machine Learning

报告人：   Yaochu Jin

 

报告简介

  Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggregated to a scalar cost function. This can be mainly attributed to the fact that most conventional learning algorithms can only deal with a scalar cost function. Over the last decade, efforts on solving machine learning problems using the Pareto-based multiobjective optimization methodology have gained increasing impetus, particularly due to the great success of multiobjective optimization using evolutionary algorithms and other population-based stochastic search methods. It has been shown that Pareto-based multiobjective learning approaches are more powerful compared to learning algorithms with a scalar cost function in addressing various topics of machine learning, such as clustering, feature selection, improvement of generalization ability, knowledge extraction, and ensemble generation. One common benefit of the different multiobjective learning approaches is that a deeper insight into the learning problem can be gained by analyzing the Pareto front composed of multiple Pareto-optimal solutions.
  This talk gives a brief overview of the existing research on multiobjective machine learning, followed by a number of case studies to illustrate the major benefits of the Pareto-based approach to machine learning, e.g., how to identify interpretable models and models that can generalize on unseen data from the obtained Pareto-optimal solutions and how to extract Pareto-optimal image features.
 

专家简介

  Yaochu Jin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. Degree from Ruhr University Bochum, Germany, in 2001.

  He is currently a Professor of Computational Intelligence and Head of the Nature Inspired Computing and Engineering (NICE) Group, Department of Computing, University of Surrey, UK. His research interests include understanding evolution, learning and development in biology and bio-inspired approached to solving engineering problems. He is an Associate Editor / Editorial Board Member of of BioSystems, Soft Computing, Evolutionary Computation, the IEEE Transactions on Cybernetics, IEEE Transactions on Nanobioscience, and the IEEE Computational Intelligence Magazine. He is an Invited Plenary / Keynote Speaker on several international conferences on various topics, including multi-objective machine learning, computational modeling of neural development, morphogenetic robotics and evolutionary aerodynamic design optimization.

  Dr Jin is Vice President for Technical Activities and an IEEE Distinguished Lecturer of the IEEE Computational Intelligence Society. He is Fellow of BCS and Senior Member of IEEE.