最 低 价：￥27.60
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作 者：（美）Richard O.Duda,Peter E.Hart,David G.Stork 著
出 版 社：机械工业出版社
出版时间：2004 年2月
I S B N：711113687X
| Richard O．Duda于麻省理工学院获得电气工程博士学位，是加州San Jose州立大学电气工程系名誉教授。他是美国人工智能学会会士。IEEE会士。 Peter E. Hart是加州Ricoh Innovations公司的创始人、总裁和CEO，同时还是理光公司的高级副总裁，此前曾任理光加州研究中心的高级副总裁。他是美国人工智能学会会士、IEEE会士，曾获IEEE信息论协会50周年论文奖。 David G.Stork于马里兰大学获得博士学位，现任加州Ricoh Innovations公司的首席科学家，同时也是斯坦福大学电气工程与计算.. << 查看详细 |
| preface 1 introduction 1.1 machine perception, 1 1.2 an example, 1 1.2.1 related fields, 8 1.3 pattern recognition systems, 9 1.3.1 sensing, 9 1.3.2 segmentation and grouping, 9 1.3.3 feature extraction, 11 1.3.4 classification, 12 1.3.5 post processing, 13 1.4 the design cycle, 14 1.4.1 data collection, 14 1.4.2 feature choice, 14 1.4.3 model choice, 15 1.4.4 training, 15 1.4.5 evaluation, 15 1.4.6 computational complexity, 16 1.5 learning and adaptation, 16 1.5.1 supervised learning, 16 .1.5.2 unsupervised learning, 17 1.5.3 reinforcement learning, 17 1.6 conclusion, 17 summary by chapters, 17 bibliographical and historical remarks, 18 bibliography, 19 2 bayesian decision theory 2.1 introduction, 20 2.2 bayesian decision theory--continuous features, 24 2.2.1 two-category classification, 25 2.3 minimum-error-rate classification, 26 2.3.1 minimax criterion, 27 *2.3.2 neyman-pearson criterion, 28 2.4 classifiers, discriminant functions, and decision surfaces, 29 2.4.1 the multicategory case, 29 2.4.2 the two-category case, 30 2.5 the normal density, 31 2.5.1 univariate density, 32 2.5.2 multivariate density, 33 2.6 discriminant functions for the normal density, 36 *2.7 error probabilities and integrals, 45 *2.8 error bounds for normal densities, 46 2.8.1 chernoffbound, 46 2.8.2 bhattacharyyabound, 47 example 2 error bounds for gaussian distributions, 48 2.8.3 signal detection theory and operating characteristics, 48 2.9 bayes decision theory--discrete features, 51 2.9.1 independent binary features, 52 example 3 bayesian decisions for three-dimensional binary data, 53 '2.10 missing and noisy features, 54 2.10.1 missing features, 54 2.10.2 noisy features, 55 *2.11 bayesian belief networks, 56 example 4 belief network for fish, 59 2.12 compound bayesian decision theory and context, 62 summary, 63 bibliographical and historical remarks, 64 problems, 65 computer exercises, 80 bibliography, 82 maximum-likelihood and bayesian 3 parameter estimation 3.1 introduction, 84 3.2 maximum-likelihood estimation, 85 3.2.1 the general principle, 85 3.2.2 the gaussian case: unknown , 88 3.2.3 the gaussian case: unknown and , 88 3.2.4 bias, 89 3.3 bayesian estimation, 90 3.3.1 the class-conditional densities, 91 3.3.2 the parameter distribution, 91 3.4 bayesian parameter estimation: gaussian case, 92 3.4.1 the univariate case: p(d), 92 3.4.2 the univariate case: p(x |
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