This edition includes discussion of Bayesian classification, Bayesian networks, linear and nonlinear classifier design (including neural networks and support vector machines), dynamic programming and hidden Markov models for sequential data, feature generation (including wavelets, principal component analysis, independent component analysis and fractals), feature selection techniques, basic concepts from learning theory, and clustering concepts and algorithms. This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering.
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This book constitutes the refereed proceedings of the Second International Conference on Advances in Pattern Recognition, ICAPR 2001, held in Rio de Janeiro, Brazil in March 2001.The 40 revised full papers presented together with three invited papers and two tutorial presentations were carefully reviewed and selected for inclusion in the proceedings. The book is organized in topical sections on neural networks and computational intelligence, character recognition and document analysis, feature selection and analysis, pattern recognition and classification, image and signal processing applications, and image feature analysis and retrieval.
本书系统介绍**化问题的稳定性分析的基本理论，讨论稳定性理论在具体优化问题中的应用，基本理论部分包括变分分析的相关素材、对偶理论、集值映射的稳定性概念及相互关系、稳定性质和微分准则、线性系统与非线性系统的稳定性.应用部分包括凸优化问题的稳定性分析、一般优化问题的稳定性分析及三类锥规刘(非线性规划、二阶锥约束优化及半定优化)问题的稳定性分析,其中三类锥规划问题的稳定性分析分别涉及**性条件、Jacobian**性条件、强二阶充分性条件、稳定性的等价刻画及孤立平稳性等内容.
本书系统介绍**化问题的稳定性分析的基本理论，讨论稳定性理论在具体优化问题中的应用，基本理论部分包括变分分析的相关素材、对偶理论、集值映射的稳定性概念及相互关系、稳定性质和微分准则、线性系统与非线性系统的稳定性.应用部分包括凸优化问题的稳定性分析、一般优化问题的稳定性分析及三类锥规刘(非线性规划、二阶锥约束优化及半定优化)问题的稳定性分析,其中三类锥规划问题的稳定性分析分别涉及**性条件、Jacobian**性条件、强二阶充分性条件、稳定性的等价刻画及孤立平稳性等内容.
本书系统介绍**化问题的稳定性分析的基本理论，讨论稳定性理论在具体优化问题中的应用，基本理论部分包括变分分析的相关素材、对偶理论、集值映射的稳定性概念及相互关系、稳定性质和微分准则、线性系统与非线性系统的稳定性.应用部分包括凸优化问题的稳定性分析、一般优化问题的稳定性分析及三类锥规刘(非线性规划、二阶锥约束优化及半定优化)问题的稳定性分析,其中三类锥规划问题的稳定性分析分别涉及**性条件、Jacobian**性条件、强二阶充分性条件、稳定性的等价刻画及孤立平稳性等内容.
