How to make a Kernel Support Vector Machine using Shogun?

This recipe helps you to make a Kernel Support Vector Machine using Shogun.

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Recipe Objective

This recipe explains how to make a Kernel Support Vector Machine using Shogun.
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Kernel Support Vector Machine

Kernel Support Vector Machine computes data-separating hyperplane in Hilbert space caused by a positive definite kernel. It is a binary classifier. We choose the hyperplane to maximize the margins between the two classes. We need to minimize the loss function.

x_train = RealFeatures(feats_train)
x_test = RealFeatures(feats_test)
y_train = BinaryLabels(labels_train)
y_test = BinaryLabels(labels_test)

svm.train()
predict = svm.apply_binary(x_test)

eval = AccuracyMeasure()
accuracy = eval.evaluate(predict, y_test)

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