'Bayes decision theory' diaporamas de présentation

Text Categorization

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Posterior, likelihood, evidence P( ? j | x) = P(x | ? j ) . P ( ? j ) / P(x) Where in case of two categories Posterio

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Posterior, likelihood, evidence

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Lecture 20 Object recognition I

Lecture 20 Object recognition I. Pattern and pattern classes Classifiers based on Bayes Decision Theory Recognition based on decision-theoretical methods Optimum statistical classifiers Pattern recognition with Matlab. Patterns and Pattern classes.

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The basics of Bayes decision theory

The basics of Bayes decision theory. Statistical pattern recognition. The most popular approach to pattern classification It has a firm background in mathematics (statistics) It offers practically applicable solutions It seeks to solve the classification task

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TRAFFIC SIGN SEGMENTATION AND RECOGNITION IN SCENE IMAGES

TRAFFIC SIGN SEGMENTATION AND RECOGNITION IN SCENE IMAGES. Fei Qin1, Bin Fang1, Hengjun Zhao1 1. Department of Computer Science, Chongqing University, Chongqing 400030, China E-mail: jiemy@cqu.edu.cn, fb@cqu.edu.cn, zhhj@cqu.edu.cn. ???? ?????? 9827208 ???? ???. ??. 1. INTRODUCTION

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