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Support vector machine
Set of methods for supervised statistical learning
Support vector machine
In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories, SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). In addition to performing linear classification, SVMs can efficiently perform non-linear classification using the kernel trick, representing the data only through a set of pairwise similarity comparisons between the original data points using a kernel function, thereby transforming them into coordinates in a higher-dimensional feature space. Thus, SVMs use the kernel trick to implicitly map their inputs into high-dimensional feature spaces, where linear classification can be performed. Furthermore, the computational complexity arising from using the kernel trick warrants lesser usage of resources...
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