|Machine learning and
||It has been suggested that Kernel trick be merged into this article. (Discuss) Proposed since November 2012.|
In computer science, kernel methods are a class of algorithms for pattern analysis, whose best known member
|Machine learning and
||It has been suggested that Kernel trick be merged into this article. (Discuss) Proposed since November 2012.|
In computer science, 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 of these tasks, data have to be represented as feature vectors, but kernel methods replace this representation by similarities to other data points.
Kernel methods owe their name to the use of kernel functions, which enable them to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. This operation is often computationally cheaper than the explicit computation of the coordinates. This approach is called the kernel trick. Kernel functions have been introduced for sequence data, graphs, text, images, as well as vectors.
Algorithms capable of operating with kernels include Support vector machine (SVM), Gaussian processes, principal components analysis (PCA), canonical correlation analysis, ridge regression, spectral clustering, linear adaptive filters and many others. Any linear model can be turned into a non-linear model by applying the "kernel trick" to the model: replacing its features (predictors) by a kernel function.
Most kernel algorithms are based on convex optimization or eigenproblems, are computationally efficient and statistically well-founded. Typically, their statistical properties are analyzed using statistical learning theory (for example, using Rademacher complexity).
Application areas of kernel methods are diverse and include geostatistics, kriging, inverse distance weighting, 3D reconstruction, bioinformatics, chemoinformatics, information extraction and handwriting recognition.
- Honarkhah, M and Caers, J, 2010, Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling, Mathematical Geosciences, 42: 487 - 517
||This article includes a list of references, but its sources remain unclear because it has insufficient inline citations. (January 2011)|
- J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
- W. Liu, J. Principe and S. Haykin. Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley, 2010.