Statistical Pattern Recognition: A Review Export

@misc{citeulike:4846865, abstract = {The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been deserving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost fifty years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New Department of Computer Science and Engineering, Michigan State University, East L...}, author = {Jain, Anil K. and Jain fellow, Anil K. and Mao, Jianchang and Duin, Robert P. W. and Jianchang Mao senior Member}, citeulike-article-id = {4846865}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.25.7821}, keywords = {mining}, posted-at = {2009-11-06 01:30:57}, priority = {2}, title = {Statistical Pattern Recognition: A Review}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.25.7821}, year = {2000} }

TY - ELEC ID - citeulike:4846865 L3 - citeulike-article-id:4846865 N2 - The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been deserving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost fifty years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New Department of Computer Science and Engineering, Michigan State University, East L... TI - Statistical Pattern Recognition: A Review KW - mining AU - Jain, Anil AU - Jain fellow, Anil AU - Mao, Jianchang AU - Duin, Robert AU - Jianchang Mao senior Member PY - 2000/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.25.7821 ER -