EAN: 9783319410623

SPRINGERBRIEFS IN COMPUTER SCIENCE SUPPORT VE...

This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector
Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning
tasks  including classification. SVMs are associated with maximizing the margin between two
classes. The concerned optimization problem is a convex optimization guaranteeing a globally
optimal solution. The weight vector associated with SVM is obtained by a linear combination of
some of the boundary and noisy vectors. Further  when the data are not linearly separable 
tuning the coefficient of the regularization term becomes crucial. Even though SVMs have
popularized the kernel trick  in most of the practical applications that are high-dimensional 
linear SVMs are popularly used. The text examines applications to social and information
networks. The work also discusses another popular linear classifier  the perceptron  and
compares its performance with that of the SVM in different application areas.

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