![]() This option allows to select the way the explanatory data are rescaled. ![]() This parameter can be used to speed up computations. Tolerance: this value define the tolerance when comparing 2 values during the optimization.Epsilon: this is a machine dependent accuracy parameter, its default value is 1x10^-12.C: this is the regularization parameter (see the description for more details).This option allows to tune the optimization algorithm to your specific needs. Support Vector Machine is available under the Machine Learning menu in XLSTAT. SVM method was generalized to be applied to regression problem or time series prediction. One versus all: one binary model per class is generated, where the corresponding class is kept and all the other classes are merged in one class. One versus one: one binary model per pair of classes is generated. XLSTAT proposes two different methods to solve multi-class problem: They all use the same principle: transform the multi-class problem in several binary problems. In its simplest form, the linear and separable case, the algorithm will select a hyperplane that separates the set of observations into two distinct classes in a way that maximizes the distance between the hyperplane and the closest observation of the training set.īecause SVM can only resolve binary problems, different methods have been developed to solve multi-class problems. The SVM aims to find a separation between two classes of objects with the idea that the larger the separation, the more reliable the classification. Since then, the SVM has known numerous developments and gained popularity in various areas such as Machine Learning, optimization, neural networks or functional analysis. ![]() It was not until the mid-90s that an algorithm implementation of the SVM was proposed with the introduction of the kernel trick (Boser, B., Guyon, I., & Vapnik, V., 1992) and the generalization to the non separable case (Cortes, C. The Support Vector Machine (SVM) is a supervised machine learning technique that was invented by Vapnik and Chervonenkis in the context of the statistical learning theory (Vapnik and Chervonenkis, 1964). Use this method to perform a binary classification, a multi-class classification or a regression on a set of observations described by qualitative and/or quantitative variables (predictors).
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