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In machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers that employ a kernel function to compute the similarity of unseen samples to training samples. The algorithm was invented in 1964,^[1] making it the first kernel classification learner.^[2]

^ Aizerman, M. A.; Braverman, Emmanuel M.; Rozoner, L. I. (1964). "Theoretical foundations of the potential function method in pattern recognition learning". Automation and Remote Control. 25: 821–837. Cited in Guyon, Isabelle; Boser, B.; Vapnik, Vladimir (1993). Automatic capacity tuning of very large VC-dimension classifiers. Advances in neural information processing systems. CiteSeerX 10.1.1.17.7215.
^ Bordes, Antoine; Ertekin, Seyda; Weston, Jason; Bottou, Léon (2005). "Fast kernel classifiers with online and active learning". JMLR. 6: 1579–1619.

Kernel perceptron