In
machine learning,
kernel methods are a class of algorithms for
pattern analysis, whose best known member is the
support vector machine (SVM). 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 pairs of data points in raw representation.