SVMTorch: Support Vector Machines for Large-Scale Regression Problems

SVMTorch : Support Vector Machines for Large

Support Vector Machines (SVMs) for regression problems are trained by solving a quadratic optimization problem which needs on the order of l 2 memory and time resources to solve, where l is the number of training examples. In this paper, we propose a decomposition algorithm, SVMTorch 1 , which is similar to SVM-Light proposed by Joachims (1999) for classiication problems, but adapted to regress...

A New Play-off Approach in League Championship Algorithm for Solving Large-Scale Support Vector Machine Problems

There are many numerous methods for solving large-scale problems in which some of them are very flexible and efficient in both linear and non-linear cases. League championship algorithm is such algorithm which may be used in the mentioned problems. In the current paper, a new play-off approach will be adapted on league championship algorithm for solving large-scale problems. The proposed algori...

Support Vector Machines for Large-Scale Regression Problems

On the Convergence of SVMTorch, An Algorithm for Large-Scale Regression Problems

Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data

This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values.  Seismic surveying was performed next on these models. F...