Abstract
In this chapter, we discuss the support vector machine (SVM), an approach for classification that was developed in the computer science community in the 1990s and that has grown in popularity since then. SVMs have been shown to perform well in a variety of settings, and are often considered one of the best “out of the box” classifiers.
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Notes
- 1.
hyperplane
The word affine indicates that the subspace need not pass through the origin.
- 2.
By expanding each of the inner products in (9.19), it is easy to see that f(x) is a linear function of the coordinates of x. Doing so also establishes the correspondence between the α i and the original parameters β j .
- 3.
With this hinge-loss + penalty representation, the margin corresponds to the value one, and the width of the margin is determined by \(\sum \beta _{j}^{2}\).
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© 2013 Springer Science+Business Media New York
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James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). Support Vector Machines. In: An Introduction to Statistical Learning. Springer Texts in Statistics, vol 103. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7138-7_9
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DOI: https://doi.org/10.1007/978-1-4614-7138-7_9
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