Kernel Methods and Support Vector Machines
Over the past ten years kernel methods such as Support Vector Machines and Gaussian Processes have become a staple for modern statistical estimation and machine learning. The groundwork for this field was laid in the second half of the 20th century by Vapnik and Chervonenkis (geometrical formulation of an optimal separating hyperplane, capacity measures for margin classifiers), Mangasarian (linear separation by a convex function class), Aronszajn (Reproducing Kernel Hilbert Spaces), Aizerman, Braverman, and Rozonoer (nonlinearity via kernel feature spaces), Arsenin and Tikhonov (regularization and ill-posed problems), and Wahba (regularization in Reproducing Kernel Hilbert Spaces). However, it took until the early 90s until positive definite kernels became a popular and viable means of estimation. Firstly this was due to the lack of sufficiently powerful hardware, since kernel methods require the computation of the socalled kernel matrix, which requires quadratic storage in the number of data points (a computer of at least a few megabytes of memory is required to deal with 1000+ points). Secondly, many of the previously mentioned techniques lay dormant or existed independently and only recently the (in hindsight obvious) connections were made to turn this into a practical estimation tool. Nowadays, a variety of good reference books exist and anyone serious about dealing with kernel methods is recommended to consult one of the following works for further information [15, 5, 8, 12]. Below, we will summarize the main ideas of kernel method and support vector machines, building on the summary given in [13].