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Application of the SVM regression
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SVM (Support Vector Machine) is made by the Cortes and Vapnik in 1995 was the first, in solving nonlinear and multidimensional pattern recognition of small samples, shows many unique advantages and fitting can be applied to functions and other machine learning problems. Support vector machine method is established in statistics learning theory of VC dimension theory and structure risk minimum principle based Shang of, according to limited of sample information in model of complexity (that on specific training sample of learning precision, Accuracy) and learning capacity (that no errors to recognition arbitrary sample of capacity) Zhijian sought best folding compromise, to obtained best of promotion capacity (or said generalization capacity). SVM MATLAB source code, including classification, fitting and regression.
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