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Seminar abstract




Abstract :

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.


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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