support vector machine下载

weixin_39820535 2020-06-01 09:00:31

pdf ebook for support vector machine.In English.
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Main features are: - Except for the QP solver, all parts are written in plain Matlab. This guarantees for easy modification. Special kinds of kernels that require much computation (such as the Fisher kernel, which is based on a model of the data) can easily be incorporated. - Extension to multi-class problems via error correcting output codes is included. - Unless many other SVM toolboxes, this one can handle SVMs with 1norm or 2norm of the slack variables. - For both cases, a decomposition algorithm is implemented for the training routine, together with efficient working set selection strategies. The training algorithm uses many of the ideas proposed by Thorsten Joachims for his SVMlight. It thus should exhibit a scaling behaviour that is comparable to SVMlight.

The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area. Contributors: Peter Bartlett, Kristin P. Bennett, Christopher J. C. Burges, Nello Cristianini, Alex Gammerman, Federico Girosi, Simon Haykin, Thorsten Joachims, Linda Kaufman, Jens Kohlmorgen, Ulrich Kreßel, Davide Mattera, Klaus-Robert Müller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Rätsch, Bernhard Schölkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson.

支持向量机matlab程序 Support Vector Machine，希望对大家有帮助

Image recognition belongs to the nonlinear classification problem,which has a certain difficulty in the process of image recognition. Image recognition based on optical wavelet and support vector machine is proposed in the paper. Optical wavelet is used to extract the features of images and support vector machine is used to create the image recognition model by utilizing the features.

支持向量机（Support Vector Machine, SVM）是一类按监督学习（supervised learning）方式对数据进行二元分类的广义线性分类器（generalized linear classifier），其决策边界是对学习样本求解的最大边距超平面（maximum-margin hyperplane）。

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