Building support vector machines with reduced classifier complexity
US7630945B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 10, 2006 |
| Grant date | Dec 8, 2009 |
| Priority date | — |
| Expiry date | Mar 19, 2027 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/2411
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem a primal system and method with the following properties has been devised: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size (dmax) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as O(ndmax2) where n is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.