An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

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Deep multilayer multiple kernel learning (2015, methodology): “Support vector machine (SVM) [9, 10] and kernel principal component analysis (KPCA) [18] are the most popular learning algorithms based on kernel methods.”
Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video (2009, background): “Support vector machines (SVM) [73, 74]are a group of models designed to find the optimal hyperplane”
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) (2005, unknown): “More comprehensive treatments can be found in Vapnik [1995], Cristianini and Shawe-Taylor [2000] and Schölkopf and Smola [2002].”
Kernel k-means: spectral clustering and normalized cuts (2004, background): “Kernel-based learning methods have appeared in a number of other areas, especially in the area of Support Vector Machines [ 3 ].”
Finding latent code errors via machine learning over program executions (2004, background): “A support vector machine (SVM) [2] tries to separate the labeled points via mathematical functions called kernel functions.”
Machine learning: a review of classification and combining techniques (2006, unknown): “An excellent survey of SVMs can be found in (Burges 1998), and a more recent book is by (Cristianini and Shawe-Taylor 2000).”