VECTOR CLASSIFIER AND VECTOR CLASSIFICATION METHOD THEREOF

• US 20120095947A1
• Filed: 07/22/2011
• Published: 04/19/2012
• Est. Priority Date: 10/18/2010
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: Yoon, Sanghun, Lyuh, Chun-Gi, Chun, Ik Jae, Suk, Jung Hee, Roh, Tae Moon
• Application Number:
  US13/189,345
• Publication Number:
  US 20120095947A1
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]

Abstract

Provided is a vector classifier and a vector classification method. The vector classifier includes a vector compressor configured to compress an input vector; a support vector storage unit configured to store a compressed support vector; and a support vector machine operation unit configured to receive the compressed input vector and the compressed support vector and perform an arithmetic operation according to a classification determining equation.

Claims

1. A vector classifier, comprising:

a vector compressor configured to compress an input vector;

a support vector storage unit configured to store a compressed support vector; and

a support vector machine operation unit configured to receive the compressed input vector and the compressed support vector and perform an arithmetic operation according to a classification determining equation.

2. The vector classifier of claim 1, wherein the classification determining equation satisfies

3. The vector classifier of claim 2, wherein the classification kernel function is linear.

4. The vector classifier of claim 2, wherein the classification kernel function is polynomial.

5. The vector classifier of claim 2, wherein the classification kernel function is a nonlinear Radial Basis Function (RBF).

6. The vector classifier of claim 5, wherein the vector compressor compresses the input vector for reducing influences of the support vector.

7. The vector classifier of claim 6, wherein for compressing the input vector,

8. The vector classifier of claim 7, wherein the compressed input vector is XV_s(:

,1;

P), and the compressed support vector is U_sD_s(;

,1;

P).

9. The vector classifier of claim 7, wherein the support vector storage unit comprises a storage space as much as a value of multiplying a degree of the input vector by a degree of the compressed input vector and a storage space as much as a value of multiplying a degree of multiplying the compressed support vector by the degree of the compressed input vector.

10. The vector classifier of claim 1, wherein the support vector machine operation unit comprises:

a kernel calculator configured to receive the compressed input vector and the compressed support vector and calculate a kernel value according to a predetermined classification kernel function;

a multiplier configured to multiply a weight which corresponds to the calculated kernel value by the kernel value;

a register configured to accumulate output values of the multiplier; and

a filter configured to generate a classification value from the accumulated value of the register using a sign function.

11. The vector classifier of claim 1, further comprising a support vector machine trainer configured to output the compressed support vector.

12. The vector classifier of claim 11, wherein the support vector machine trainer comprises:

a training vector storage unit configured to store training vectors;

a training vector compression unit configured to compress a training vector outputted from the training vector storage unit; and

a vector training unit configured to select a support vector using the compressed training vector.

13. A vector classification method of a vector classifier, comprising:

compressing an input vector for reducing a degree of the input vector; and

classifying according to a classification determining equation receiving the compressed input vector and a compressed support vector.

14. The vector classification method of claim 13, further comprising compressing a support vector.

15. The vector classification method of claim 13, wherein the compressing the support vector comprises compressing a training vector and selecting a support vector using the compressed training vector.

16. The vector classification method of claim 15, wherein the selected support vector is the compressed support vector.