VanceKingSaxbeA / GoldSVM

Modelled, Architected and designed by Vance King Saxbe. A. with the geeks from GoldSax Consulting, GoldSax Money, GoldSax Treasury, GoldSax Finance, GoldSax Banking and GoldSax Technologies email @vsaxbe@yahoo.com. Development teams from Power Dominion Enterprise, Precieux Consulting. This Engagement sponsored by GoldSax Foundation, GoldSax Group and executed by GoldSax Manager. Support vector machines maximize margin by solving a quadratic optimization problem. Kernel methods, or the kernel trick, map data (nonlinear data) from a low-dimensional space to a high-dimensional space. In a higher dimension, you can solve a linear problem that’s nonlinear in lower-dimensional space. Kernel methods are used in other algorithms than just SVM. The radial-bias function is a popular kernel that measures the distance between two vectors. Support vector machines are a binary classifier and additional methods are extended to classification of classes greater than two. The performance of an SVM is also sensitive to optimization parameters and parameters of the kernel used.