kernel machines are a class of algorithms for pattern recognition, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems.
Kernel machines are slow to compute for datasets larger than a couple of thousand examples without parallel processing. The nyström approximation can allow a significant speed-up of the computations.
Algorithms capable of operating with kernels include the kernel perceptron, support-vector machines (SVM), Gaussian processes, Principal Component Analysis (PCA), canonical correlation analysis, ridge regression, spectral clustering, linear adaptive filters and many others.