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SVM - Support Vector Machines |
 | | In fact, a SVM model using a sigmoid kernel function is equivalent to a two-layer, feed-forward neural network. |
 | | Using a kernel function, SVM’s are an alternative training method for polynomial, radial basis function and multi-layer perceptron classifiers in which the weights of the network are found by solving a quadratic programming problem with linear constraints, rather than by solving a non-convex, unconstrained minimization problem as in standard neural network training. |
 | | So the goal of SVM modeling is to find the optimal hyperplane that separates clusters of vector in such a way that cases with one category of the target variable are on one side of the plane and cases with the other category are on the other size of the plane. |
| www.dtreg.com /svm.htm (1804 words) |