最 低 价：￥51.80
| 随着人工智能、信息检索和海量数据处理等技术的发展，模式识别成为了研究热点。 在本书中，ripley将模式识别领域中的统计方法和基于神经网络的机器学习这两个关键思想结合起来，以统计决策理论和计算学习理论为依据。建立了神经网络理论的坚实基础。在理论层面。本书强调概率与统计；在实践层面，则强调模式识别的实用方法。 本书已被国际知名大学采用为教材，对于研究模式识别和神经网络的专业人士，也是不可不读的优秀参考书。 |
| 里普利（B.D.Ripley）著名的统计学家，牛津大学应用统计教授。他在空间统计学、模式识别领域作出了重要贡献，对S的开发以及S-PLUSUS和R的推广应用有着重要影响。20世纪90年代他出版了人工神经网络方面的著作，影响很大，引导统计学者开始关注机器学习和数据挖掘。除本书外，他还著有Modern Applied Statistics with S和S Programming。 |
| 1 introduction and examples 1.1 how do neural methods differ? 1.2 the patterm recognition task 1.3 overview of the remaining chapters 1.4 examples 1.5 literature 2 statistical decision theory 2.1 bayes rules for known distributions 2.2 parametric models 2.3 logistic discrimination 2.4 predictive classification 2.5 alternative estimation procedures 2.6 how complex a model do we need? 2.7 performance assessment 2.8 computational learning approaches 3 linear discriminant analysis 3.1 classical linear discriminatio 3.2 linear discriminants via regression 3.3 robustness 3.4 shrinkage methods 3.5 logistic discrimination 3.6 linear separatio andperceptrons 4 flexible diseriminants 4.1 fitting smooth parametric functions 4.2 radial basis functions 4.3 regularization 5 feed-forward neural networks 5.1 biological motivation 5.2 theory 5.3 learning algorithms 5.4 examples 5.5 bayesian perspectives 5.6 network complexity 5.7 approximation results 6 non-parametric methods 6.1 non-parametric estlmation of class densities 6.2 nearest neighbour methods 6 3 learning vector quantization 6.4 mixture representations 7 tree-structured classifiers 7.1 splitting rules 7.2 pruning rules 7.3 missing values 7.4 earlier approaches 7.5 refinements 7.6 relationships to neural networks 7.7 bayesian trees 8 belief networks 8.1 graphical models and networks 8.2 causal networks 8 3 learning the network structure 8.4 boltzmann machines 8.5 hierarchical mixtures of experts 9 unsupervised methods …… 10 finding good pattern features a statistical sidelines glossary references author index subject index |
内容加载中，请稍后...
商品评论（0条）