Patent ID: 7395253
Filing Date: 2008-07-01
Classification: G06K

Abstract:
1. A computer-implemented method of classifying numerical data sets comprising the steps of: defining an input matrix A with m rows and n columns representing a set of m numerical data points having an input space with a dimension of n, wherein n corresponds to a number of features associated with the numerical data set and each row belongs to either class A+ or A−, and further wherein the numerical data set represents medical data; generating a support vector machine by solving a quadratic programming problem corresponding to the input matrix, wherein the quadratic programming problem is defined by a positive definite matrix Q: wherein H=D[A−e], H′ is the transpose of H, I is an identity matrix, e is a vector of ones, D is a diagonal matrix of plus and minus ]'s wherein a value on a diagonal of the D matrix is +1 if the corresponding row of the A matrix is in the class of A+ and −1 if the correspondina row of the A matrix is in the class of A−, and v is a parameter associated with a distance between a pair of parallel bounding planes; calculating a linear separating surface with the support vector machine by iteratively calculating a value u defined by: wherein and the + subscript replaces negative components by zeros; and dividing the set of numerical data into a plurality of subsets of data using the linear separating surface in the n-dimensional x space: x′w=γ, where x′ is the transpose of a vector x, w is orthogonal to the separating surface and γ locates the separating surface relative to an origin, wherein the plurality of subsets include at least a good prognostic set of medical data and a poor prognostic set of medical data that are located on opposite sides of the separating surface.