Support Vector Machines for Classification and Regression

作者：S.R. Gunn · 年份：1998 · 被引用次数：2827 · 研究领域：Neural Networks and Applications、Face and Expression Recognition、Blind Source Separation Techniques

The problem of empirical data modelling is germane to many engineering applications. \nIn empirical data modelling a process of induction is used to build up a model of the \nsystem, from which it is hoped to deduce responses of the system that have yet to be observed. \nUltimately the quantity and quality of the observations govern the performance \nof this empirical model. By its observational nature data obtained is finite and sampled; \ntypically this sampling is non-uniform and due to the high dimensional nature of the \nproblem the data will form only a sparse distribution in the input space. Consequently \nthe problem is nearly always ill posed (Poggio et al., 1985) in the sense of Hadamard \n(Hadamard, 1923). Traditional neural network approaches have suffered difficulties with \ngeneralisation, producing models that can overfit the data. This is a consequence of the \noptimisation algorithms used for parameter selection and the statistical measures used \nto select the ’best’ model. The foundations of Support Vector Machines (SVM) have \nbeen developed by Vapnik (1995) and are gaining popularity due to many attractive \nfeatures, and promising empirical performance. The formulation embodies the Structural \nRisk Minimisation (SRM) principle, which has been shown to be superior, (Gunn \net al., 1997), to traditional Empirical Risk Minimisation (ERM) principle, employed by \nconventional neural networks. SRM minimises an upper bound on the expected risk, \nas opposed t...