Approximate Stochastic Subgradient Estimation Training for Support Vector Machines

ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines

Subgradient methods for SVMs have been successful in solving the primal formulation with linear kernels. The approach is extended here to nonlinear kernels, and the assumption of strong convexity of the objective is dropped, allowing an intercept term to be used in the classifier.

Stochastic Subgradient Methods

Stochastic subgradient methods play an important role in machine learning. We introduced the concepts of subgradient methods and stochastic subgradient methods in this project, discussed their convergence conditions as well as the strong and weak points against their competitors. We demonstrated the application of (stochastic) subgradient methods to machine learning with a running example of tr...

Stochastic Subgradient Approach for Solving Linear Support Vector Machines – an Overview

This paper is an overview of a recent approach for solving linear support vector machines (SVMs), the PEGASOS algorithm. The algorithm is based on a technique called the stochastic subgradient descent and employs it for solving the optimization problem posed by the soft margin SVM a very popular classifier. We briefly introduce the SVM problem and one of the widely used solvers, SVM light, then...

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

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...