ABSTRACT
Support vector machines (SVMs), including support vector classifier (SVC) and support vector regressor (SVR), are among the most robust and accurate methods in all well-known data mining algorithms. SVMs, which were originally developed by Vapnik in the 1990s [1-11], have a sound theoretical foundation rooted in statistical learning theory, require only as few as a dozen examples for training, and are often insensitive to the number of dimensions. In the past decade, SVMs have been developed at a fast pace both in theory and practice.
