In machine learning, kernel machines are a class of algorithms for pattern
analysis, whose best known member is the support-vector machine (SVM). These
methods involve using linear classifiers to solve nonlinear problems. The
general task of pattern analysis is to find and study general types of
relations (for example clusters, rankings, principal components, correlations,
classifications) in datasets. For many algorithms that solve these tasks, the
data in raw representation have to be explicitly transformed into feature
vector representations via a user-specified feature map : in contrast,
kernel methods require only a user-specified kernel , i.e., a similarity
function over all pairs of data points computed using inner products.