Gradient based training method for a support vector machine

• US 8,005,293 B2
• Filed: 04/11/2001
• Issued: 08/23/2011
• Est. Priority Date: 04/11/2000
• Status: Active Grant

• Current Assignee: Telstra Corporation Limited (Telstra Group Ltd.)
• Original Assignee: Telstra New Wave Pty Limited
• Inventors: Anderson, Trevor Bruce, Kowalczyk, Adam
• Primary Examiner(s): Carter; Aaron W
• Application Number:
  US10/257,929
• Publication Number:
  US 20030158830A1
• Time in Patent Office: 3,786 Days
• Field of Search: 382155-161, 382/225, 382/253, 706 12- 14
• US Class Current: 382/155
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]

Abstract

A training method for a support vector machine, including executing an iterative process on a training set of data to determine parameters defining the machine, the iterative process being executed on the basis of a differentiable form of a primal optimization problem for the parameters, the problem being defined on the basis of the parameters and the data set.

Claims

1. A training method for a support vector machine to perform data classification for a relationship between a training set of data, the method executed by a computer system, including executing an iterative process by a processor on the training set of data read from a data input device to determine parameters defining said machine represented by:

y=sgn(w·

x+β

b),

where y is the classification output which is output by a data output device, x is the input data read from the data input device, β

is 0 or 1, the vector w and bias b, being parameters defining a decision surface, said iterative process being executed by the processor based on a derivative optimization function for said parameters and said data set.

2. A method as claimed in claim 1, wherein said method is adapted for generation of a kernel learning machine.

3. A method as claimed in claim 1, wherein said method is adapted to generate a regularisation network.

4. A method as claimed in claim 1, wherein for classification, said parameters are obtained by minimizing the differentiable objective function:

5. A method as claimed in claim 4, wherein said iterative process operates on a derivative of the objective function Ψ

until the vectors converge to a vector w for the machine.

6. A method as claimed in claim 1, wherein for ε

-insensitive regression, the vector w and bias b, being parameters defining a decision surface, are obtained by minimizing the differentiable objective function

7. A support vector machine for performing a classification task, the support vector machine comprising:

an input device reading training data points;

a processor calculating classification output y for the classification task given by

8. A support vector machine for ε

-regression, the support vector machine comprising;

a data input device reading training data points; and

a processor calculating an a classification output y given by

9. A regularization network of a computer system comprising:

a data input device for reading a set of training data points, x;

a processor calculating a classification output y by solving the equation

10. A non-transitory computer readable medium having stored thereon instructions for performing data classification for the relationship between a training set of data, the stored instructions comprising machine executable code, which when executed by at least one machine processor, causes the machine to:

execute an iterative process on the training set of data to determine parameters defining said machine represented by;

y=sgn(w.x+β

b),

where y is the output which is output by a data output device, x is the input data read from the data input device, β

is 0 or 1, the vector w and bias b, being parameters defining a decision surface, said iterative process being executed on a derivative optimization function for said parameters and said data set.