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Clojure for Machine Learning by Akhil Wali

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Using kernel SVMs

In some cases, the available training data is not linearly separable and we would not be able to model the data using linear classification. Thus, we need to use different models to fit nonlinear data. As described in Chapter 4, Building Neural Networks, ANNs can be used to model this kind of data. In this section, we will describe how we can fit an SVM on nonlinear data using kernel functions. An SVM that incorporates kernel function is termed as a kernel support vector machine. Note that, in this section, the terms SVM and kernel SVM are used interchangeably. A kernel SVM will classify data based on a nonlinear decision boundary, and the nature of the decision boundary depends on the kernel function that is used by the SVM. ...

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