FAFU机器学习08-1 Support Vector Machine课件VIP专享VIP免费

Foundations of Machine LearningSupport Vector Machine （支持向量机）Top 10 algorithms in data miningC4.5K-MeansSVMAprioriEM (Maximum Likelihood)PageRankAdaBoostKNNNaïveBayesCARTSupport Vector Machine背景间隔与支持向量对偶问题核函数软间隔与正则化支持向量回归SVM in sklearnSupport Vector MachineLesson 7 - 3Background The original SVM algorithm was invented by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963 Maximal Margin Classifier In 1992, Bernhard E. Boser, Isabelle M. Guyon and Vladimir N. Vapnik suggested a way to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The kernelized version using the Kernel Trick The current standard incarnation (soft margin) was proposed by Corinna Cortes and Vapnik in 1993 and published in 1995. Soft Margin Classifier The soft-margin kernelized version (which combine 1, 2 and 3)Support Vector MachineLesson 7 - 4BackgroundIn 1996, Vapnik et al. proposed a version of SVM to perform regression instead of classification.  Support Vector Regression (SVR) In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.  However, it is mostly used in classification problems. SVM becomes popular because of its success in handwritten digit recognition Support Vector MachineLesson 7 - 5 Pros Kernel-based framework is very powerful, flexible Work very well in practice, even with very small training sample sizes Solution can be formulated as a quadratic programming Many publicly available SVM packages: e.g. LIBSVM, LIBLINEAR, SVMLight  Cons Can be tricky to select best kernel function...