![支持向量机导论（英文影印版）[按需印刷]](http://images.china-pub.com/ebook25001-30000/26228/zcover.jpg)
最 低 价：￥58.00
定 价：￥29.00
作 者：（美）Nello Cristianini,（英）John Shawe-Taylor 著
出 版 社：机械工业出版社
出版时间：2005 年8月
I S B N：7111167899
| Nello Cristlanini 先后在意大利的里雅斯特大学、英国伦敦大学皇家豪勒威学院、英国布里斯投大学、美国加州大学圣克鲁兹分校学习。他是支持向量机与其他学习系统的理论与应用方面卓有成就的年青研究人员，在各种杂志和国际学术会议上发表了许多有关这一领域的论文。 John Shawe-Taylor 先后在英国剑桥大学、位子斯洛文尼亚的卢布尔雅那大学、加拿大西蒙·弗雷泽大学、英国伦敦大学帝国学院、英国伦敦大学皇家豪勒威学院学习。他发表了许多有关学习系统以及离散数学和计算机科学等领域.. << 查看详细 |
| preface notation 1 the learning methodology 1.1 supervised learning 1.2 learning and generalisation 1.3 improving generalisation 1.4 attractions and drawbacks of learning 1.5 support vector machines for learning 1.6 exercises 1.7 further reading and advanced topics 2 linear learning machines 2.1 linear classification 2.1.1 rosenblatt's perceptron 2.1.2 other linear classifiers 2.1.3 multi-class discrimination 2.2 linear regression 2.2.1 least squares 2.2.2 ridge regression 2.3 dual representation of linear machines 2.4 exercises .2.5 further reading and advanced topics 3 kernel-induced feature spaces 3.1 learning in feature space 3.2 the implicit mapping into feature space 3.3 making kernels 3.3.1 characterisation of kernels 3.3.2 making kernels from kernels 3.3.3 making kernels from features 3.4 working in feature space 3.5 kernels and gaussian processes 3.6 exercises 3.7 further reading and advanced topics 4 generalisation theory 4.1 probably approximately correct learning 4.2 vapnik chervonenkis (vc) theory 4.3 margin-based bounds on generalisation 4.3.1 maximal margin bounds 4.3.2 margin percentile bounds 4.3.3 soft margin bounds 4.4 other bounds on generalisation and luckiness 4.5 generalisation for regression 4.6 bayesian analysis of learning 4.7 exercises 4.8 further reading and advanced topics 5 optimisation theory 5.1 problem formulation 5.2 lagrangian theory 5.3 duality 5.4 exercises 5.5 further reading and advanced topics 6 support vector machines 6.1 support vector classification 6.1.1 the maximal margin classifier 6.1.2 soft margin optimisation 6.1.3 linear programming support vector machines 6.2 support vector regression 6.2.1 8-insensitive loss regression 6.2.2 kernel ridge regression 6.2.3 gaussian processes 6.3 discussion 6.4 exercises 6.5 further reading and advanced topics 7 implementation techniques 7.1 general issues 7.2 the naive solution: gradient ascent 7.3 general techniques and packages 7.4 chunking and decomposition 7.5 sequential minimal optimisation (smo) 7.5.1 analytical solution for two points 7.5.2 selection heuristics 7.6 techniques for gaussian processes 7.7 exercises 7.8 further reading and advanced topics 8 applications of support vector machines 8.1 text categorisation 8.1.1 a kernel from ir applied to information filtering 8.2 image recognition 8.2.1 aspect independent classification 8.2.2 colour-based classification 8.3 hand-written digit recognition 8.4 bioinformatics 8.4.1 protein homology detection 8.4.2 gene expression 8.5 further reading and advanced topics a pseudocode for the smo algorithm b background mathematics b.1 vector spaces b.2 inner product spaces b.3 hilbert spaces b.4 operators, eigenvalues and eigenvectors references index |
内容加载中，请稍后...
商品评论（0条）