Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
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DOI：
10.5555/299094
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发表时间：
1999-02
期刊：
影响因子：
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通讯作者：
B. Scholkopf;C. Burges;Alex Smola
中科院分区：
文献类型：
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作者：
B. Scholkopf;C. Burges;Alex Smola
介绍支持向量学习路线图。第一部分理论：三个评论：函数估计的支持向量方法，Vladimir Vapnik支持向量机和其他模式分类器的泛化性能，Peter Bartlett和John Shawe-Taylor贝叶斯投票方案和大余距分类器，Nello Cristianini和John Shawe-Taylor支持向量机，再现核Hilbert空间，以及随机化GACV， Grace Wahba几何和核方法的不变性。Christopher J.C. Burges关于边缘分类器退火VC熵的统计力学研究，Manfred Opper熵数，算子和支持向量核，Robert C. Williamson等。第2部分实现：解决支持向量分类中出现的二次规划问题，Linda Kaufman实现大规模支持向量机器学习，Thorsten Joachims使用顺序最小优化快速训练支持向量机，John C. Platt。第三部分应用：支持向量机用于混沌系统的动态重建，Davide Mattera和Simon Haykin使用支持向量机进行时间序列预测，Klaus-Robert Muller等人的配对分类和支持向量机，Ulrich Kressel。第四部分算法的扩展：降低支持向量机的运行时复杂度，Edgar E. Osuna和Federico Girosi使用ANOVA分解核的支持向量回归，Mark O. Stitson等人的支持向量密度估计，Jason Weston等人结合支持向量和数学规划方法进行分类，Bernhard Scholkopf等人。
Introduction to support vector learning roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik generalization performance of support vector machines and other pattern classifiers, Peter Bartlett and John Shawe-Taylor Bayesian voting schemes and large margin classifiers, Nello Cristianini and John Shawe-Taylor support vector machines, reproducing kernel Hilbert spaces, and randomized GACV, Grace Wahba geometry and invariance in kernel based methods, Christopher J.C. Burges on the annealed VC entropy for margin classifiers - a statistical mechanics study, Manfred Opper entropy numbers, operators and support vector kernels, Robert C. Williamson et al. Part 2 Implementations: solving the quadratic programming problem arising in support vector classification, Linda Kaufman making large-scale support vector machine learning practical, Thorsten Joachims fast training of support vector machines using sequential minimal optimization, John C. Platt. Part 3 Applications: support vector machines for dynamic reconstruction of a chaotic system, Davide Mattera and Simon Haykin using support vector machines for time series prediction, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel. Part 4 Extensions of the algorithm: reducing the run-time complexity in support vector machines, Edgar E. Osuna and Federico Girosi support vector regression with ANOVA decomposition kernels, Mark O. Stitson et al support vector density estimation, Jason Weston et al combining support vector and mathematical programming methods for classification, Bernhard Scholkopf et al.