Support Vector Machine (SVM) is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy while automatically avoiding over fit of the data.
There is no upper limit on the number of attributes and target cardinality for SVMs.
The SVM kernel functions currently supported are linear and Gaussian.
Neural networks and radial basis functions, both popular data mining techniques, can be viewed as special cases of SVMs.
SVMs perform well with real-world applications such as classifying text, recognizing hand-written characters, classifying images, as well as bioinformatics and biosequence analysis. Their introduction in the early 1990's led to an explosion of applications and deepening theoretical analysis that established SVM along with neural networks as one of the standard tools for machine learning and data mining.
One-class SVM classifiers are used for anomaly detection; for more information, see Anomaly Detection.
SVMs can be used to solve regression problems, where the target attributes have continuous values.
SVM can be used to solve regression and classification problems where the input table has one or more text columns. For details, see Text Mining.
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