| 书目名称 | Support Vector Machines for Pattern Classification |
| 编辑 | Shigeo Abe |
| 视频video | http://file.papertrans.cn/883/882148/882148.mp4 |
| 概述 | A comprehensive resource for the use of Support Vector Machines in Pattern Classification.Takes the unique approach of focussing on classification rather than covering the theoretical aspects of Suppo |
| 丛书名称 | Advances in Computer Vision and Pattern Recognition |
| 图书封面 |  |
| 描述 | .A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors.. |
| 出版日期 | Book 2010Latest edition |
| 关键词 | Fuzzy Systems; Kernel Methods; Neural Networks; Pattern Classification; Support Vector Machine; Support V |
| 版次 | 2 |
| doi | https://doi.org/10.1007/978-1-84996-098-4 |
| isbn_softcover | 978-1-4471-2548-8 |
| isbn_ebook | 978-1-84996-098-4Series ISSN 2191-6586 Series E-ISSN 2191-6594 |
| issn_series | 2191-6586 |
| copyright | Springer-Verlag London 2010 |
|Archiver|手机版|小黑屋|
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