Читать книгу Intelligent Network Management and Control - Badr Benmammar - Страница 25

The support-vector machine is a technique used for solving various learning, classification and prediction problems. The support-vector machine was employed in an implementation of the structural risk minimization (SRM) principle of Vapnik (1998), which minimizes the generalization error, in the sense of true error on unseen examples. The basic support-vector machine addresses problems with two classes, in which data are separated by a hyperplane defined by a certain number of support vectors. Support vectors are a subset of learning data serving to define the limit between the two classes. When the support-vector machine cannot separate two classes, it solves this problem by mapping the input data in spaces of high-dimensional functions by means of a kernel function. In a high-dimensional space, it is possible to create a hyperplane enabling a linear separation (which corresponds to a curved surface in the lower input space). Consequently, the kernel function plays an important role in the support-vector machine. In practice, various kernel functions can be used, such as linear, polynomial, or Gaussian. A remarkable property of the support-vector machine is its learning capacity, which does not depend on the dimensionality of the characteristic space. This means that the support-vector machine can generalize when given numerous functionalities. Mukkamala and Sung (2003b) showed the many advantages of the support-vector machine compared to other techniques. Support-vector machines surpass neural networks in terms of upgradability, learning time, runtime and prediction accuracy. Mukkamala and Sung (2003a) also applied support-vector machines for the extraction of intrusion detection characteristics of KDD files. They empirically proved that the functionalities selected using the support-vector machine yielded similar results as the use of a full set of functionalities. This decrease in the number of functionalities reduces the computation efforts. Chen et al. (2005) also proved that support-vector machines surpassed neural networks.