Lagrangian support vector machine
First Claim
Patent Images
1. A method of classifying data comprising the steps of:
- defining an input matrix representing a set of data having an input space with a dimension of n, wherein n corresponds to a number of features associated with the data set;
generating a support vector machine to solve a system of linear equations corresponding to the input matrix, wherein the system of linear equations is defined by a positive definite matrix; and
calculating a separating surface with the support vector machine to divide the set of data into at least two subsets of data.
2 Assignments
0 Petitions
Accused Products
Abstract
A Lagrangian support vector machine solves problems having massive data sets (e.g., millions of sample points) by defining an input matrix representing a set of data having an input space with a dimension of n that corresponds to a number of features associated with the data set, generating a support vector machine to solve a system of linear equations corresponding to the input matrix with the system of linear equations defined by a positive definite matrix, and calculating a separating surface with the support vector machine to divide the set of data into two subsets of data
-
Citations
50 Claims
-
1. A method of classifying data comprising the steps of:
-
defining an input matrix representing a set of data having an input space with a dimension of n, wherein n corresponds to a number of features associated with the data set;
generating a support vector machine to solve a system of linear equations corresponding to the input matrix, wherein the system of linear equations is defined by a positive definite matrix; and
calculating a separating surface with the support vector machine to divide the set of data into at least two subsets of data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
-
-
20. A method of classifying data comprising the steps of:
-
defining an input matrix representing a set of data having an input space with a dimension of n, wherein n corresponds to a number of features associated with the data set;
generating a support vector machine to solve a system of linear equations corresponding to the input matrix, wherein the system of linear equations is defined by a positive definite matrix; and
calculating a linear separating surface with the support vector machine to divide the set of data into a plurality of subsets of data. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29)
-
-
30. A method of classifying data comprising the steps of:
-
defining an input matrix representing a set of data having an input space with a dimension of n, wherein n corresponds to a number of features associated with the data set;
generating a support vector machine to solve a system of linear equations corresponding to the input matrix, wherein the system of linear equations is defined by a positive definite matrix; and
calculating a nonlinear separating surface with the support vector machine to divide the set of data into a plurality of subsets of data. - View Dependent Claims (31, 32, 33, 34, 35, 36, 37, 38)
-
-
39. A method of determining a separating surface between features of a data set comprising the steps of:
-
defining an input matrix A representing the data set having an input space with a dimension of n, wherein n corresponds to a number of the features associated with the data set;
constructing a support vector machine to define the separating surface by solving a system of linear equations corresponding to the input matrix A, wherein the system of linear equations is defined by a positive definite matrix with a dimension equal to (n+1); and
dividing the data set into a plurality of subsets of data based on the separating surface calculated by the support vector machine. - View Dependent Claims (40, 41)
-
-
42. A support vector machine comprising:
-
an input module that generates an input matrix representing a set of data having an input space with a dimension of n, wherein n corresponds to a number of features associated with the data set;
a processor that receives an input signal from the input module representing the input matrix, wherein the processor calculates an output signal representing a solution to a system of linear equations corresponding to the input signal, and the system of linear equations is defined by a positive definite matrix; and
an output module that divides the set of data into a plurality of subsets of data based on the output signal from the processor that corresponds to a separating surface between the plurality of subsets of data. - View Dependent Claims (43)
-
-
44. A method according to claim 44, wherein the separating surface is a nonlinear surface.
-
45. A method of classifying patients comprising the steps of:
-
defining an input matrix representing a set of patient data having an input space with a dimension of n, wherein n corresponds to a number of features associated with each patent in the set of patient data;
generating a support vector machine to solve a system of linear equations corresponding to the input matrix, wherein the system of linear equations is defined by a positive definite matrix; and
calculating a separating surface with the support vector machine to divide the set of patient data into a plurality of subsets of data.
-
-
46. A method according to claim 46, wherein a dimension of the positive definite matrix is equal to the dimension of (n+1).
- 47. A method according to claim 47, wherein the separating surface is a linear surface.
Specification