题目：同时实现分类和聚类的学习框架 报告人：陈松灿教授 南京航空航天大学计算机科学与工程 时间：10月13日上午10:30-11:30 地点：蒙民伟楼404会议室 摘要：
Traditional pattern recognition generally involves two tasks: unsupervised
clustering and supervised classification. When class information is available,
fusing the advantages of both clustering learning and classification learning
into a single framework is an important problem worthy of study. In this talk
, simultaneous learning frameworks for clustering and classification
respectively with respect to single-objective (SCC) and multi-objective (MSCC)
formulations are presented. Both aim to achieve three goals: (1) acquiring
the robust classification and clustering simultaneously; (2) developing an
effective and transparent classification mechanism; (3) exploring and
revealing the underlying relationship between clusters and classes.
Experimental results show that both SCC and MSCC achieve promising
classification and clustering results at one time. 简历：
Songcan Chen(陈松灿), received his B.S. degree in mathematics from Hangzhou
University (now merged into Zhejiang University) in 1983. In December. 1985,
he completed his M.S. degree in computer applications at Shanghai Jiaotong
University and then worked at the Nanjing University of Aeronautics &
Astronautics (NUAA) in January 1986 as an assistant lecturer. There he
received a Ph.D. degree, in 1997, in communication and information systems.
Since 1998, as a full-time professor, he has been with the computer science
and engineering department at NUAA. His research interests include pattern
recognition, machine learning and neural computing. In these fields, he has
authored or coauthored over 130 scientific journal papers.
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