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Kernel methods (KMs) are a class of algorithms for pattern analysis, whose best known element is the support vector machine (SVM).
• Kernel methods, including support vector machines, and the representer theorem.
• Information-theoretic learning models, including copulas, independent components analysis(ICA), coherent ICA, and information bottleneck.
The most widely used classifiers are the neural network,[106] kernel methods such as the support vector machine,[107] k-nearest neighbor algorithm,[108] Gaussian mixture model,[109] naive Bayes classifier,[110] and decision tree.
Learning techniques include reinforcement learning, optimization methods, recurrent and state space models, on-line algorithms, evolutionary computing, kernel methods, bayesian estimation, wavelets, neural nets, SVMs, boosting, ...
 See also: Neural network, Classification, Machine learning, Support vector machine, Pattern recognition
  
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