The Support Vector Machine (SVM) is a new and very promising classiication technique developed by Vapnik and his group at AT&T Bell Laboratories 3, 6, 8, 24]. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classiiers. The main idea behind the technique is to separate the classes with a surface that ma...

Consider a binary classification prediction problem. Training data are given and we denote them observations as T = {X,y} = {xi, yi}i=1, where xi ∈ R, yi ∈ {+1,−1} (accordingly X ∈ Rm×n). First, we will consider the case where the two classes are linearly separable. That is, by an n-dimensional decision boundary which is the result of an n + 1-dimensional hyperplane). Furthermore, since there c...

We reexamine the problem of transverse axial vector and vector anomalies in four-dimensional U(1) gauge theories studied in [10] using perturbative method. It is found that there are no transverse anomalies for both axial vector and vector current. A comparison between our results and those of [10] is given. PACS:11.15.-q Typeset using REVTEX

support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...