Pattern Recognition 内容 1.导论 2. Bayes决策理论 Feng Jufu 4.线性判别函数 fif@cis.pku.edu.cn ·6.统计学习理论 Center for Info 8.正则化网络 National Key Lab of Machine Perception Peking University 10.非监督学习与聚类 11.应用举例 References 主要期刊和会议 IEEE Trans. On PAMl. NN 《模式识别》,边肇祺,张学工等编著,清华大学出版社,2000 Pattern Recognition Pattern Recognition Letter Y大9出:206(计字习理论的本) Machine Learning [4] Vladimir N. Vapnik, Statistical Learning Theory, John Wiley Neural Computation 《模式识别与人工智能》 CVPR、ICPR、|CML、COLT、NPS [6]S Haykin, Neural Networks-a Comprehensive Foundation, 2nd Edition, Tsinghua University Press, Prentice Hall Press, 200 第一章导论 Introduction 模式识别简介 Pattern recognition is the study of how machines can observe the environment. learn 基本概念 to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns 模式识别方法 (Anil K. Jain 模式识别应用1 1 Pattern Recognition Feng Jufu fjf@cis.pku.edu.cn Center for Information Science National Key Lab of Machine Perception Peking University 2 内容 z 1．导论 z 2．Bayes决策理论 z 3．概率密度估计 z 4．线性判别函数 z 5．神经网络 z 6．统计学习理论 z 7．SVM z 8．正则化网络 z 9．特征空间 z 10.非监督学习与聚类 z 11. 应用举例 3 References z [1] Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, 2nd Edition, John Wiley & Sons, Inc. 2001 z [2] 《模式识别》，边肇祺，张学工等编著，清华大学出版社，2000 年1月第2版 z [3] Vladimir N. Vapnik, The Nature of Statistical Learning, Springer￾Verlag, New York, NY, 1995 (中译本《统计学习理论的本质》，张学 工译，清华大学出版社，2000年9月) z [4] Vladimir N. Vapnik, Statistical Learning Theory, John Wiley & Sons, Inc. 1998 z [5] Nello Cristianini, John Shawe-Taylor, An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000 z [6] S. Haykin, Neural Networks — a Comprehensive Foundation, 2nd Edition, Tsinghua University Press, Prentice Hall Press, 2001. 4 主要期刊和会议 z IEEE Trans. On PAMI, NN z Pattern Recognition z Pattern Recognition Letter z Machine Learning z Neural Computation z 《模式识别与人工智能》 z CVPR、ICPR、ICML、COLT、NIPS…… 5 第一章 导论 z 模式识别简介 z 基本概念 z 模式识别方法 z 模式识别应用 6 Introduction z Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns. (Anil K. Jain)