Implement Multiclass Support Vector Machine using shogun.

This recipe helps you to implement Multiclass Support Vector Machine using shogun.

Recipe Objective

This recipe explains how to implement a Multi-class Support Vector Machine using shogun.
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Multi-class Support Vector Machine

To evaluate one vs one classification multi-class support vector machine uses CLibSVM. It is a multi-class classifier. Linear Support Vector Machine makes sure that the margins between the two classes are maximized by determining a hyper-plane. It is a binary classifier. We need to minimize the loss function.

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

s = ShareBoost(x_train, y_train, 2)
s.train()
z = RealSubsetFeatures(x_test, s.get_activeset())
predict = shareboost.apply_multiclass(z)

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

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