Support vector machines: a tutorial overview and critical appraisal
Summary form only given. There has been much interest in the use of support vector machines (SVM) as an approach to high performance pattern classification. In the linearly separable case, SVMs attempt to position a class boundary so that the margin from the nearest example is maximised. This criterion can be implemented by solving a quadratic programming problem, and the solution turns out to be one in which the class boundary may be expressed as a linear combination of a subset of the training data (the support vectors). The elegance of the QP formulation, and the relationship between control of complexity in this formulation and Vapnik-Chervonenkis dimensions are seen as prime attractions of the SVM method. A related idea in high performance pattern classification is that of boosting multiple classifiers. The author shows that the standard SVM formulation is not robust to noise and explains the performance of boosting algorithms by reference to receiver operating characteristics curves.