The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 7707129 B1

PDF
Full Text
Expired
Date of Patent:
Apr. 27, 2010

Filed:

Mar. 20, 2006
Applicants:

Dong Zhuang, Beijing, CN;

Benyu Zhang, Beijing, CN;

Zheng Chen, Beijing, CN;

Hua-jun Zeng, Beijing, CN;

Jian Wang, Beijing, CN;

Inventors:

Dong Zhuang, Beijing, CN;

Benyu Zhang, Beijing, CN;

Zheng Chen, Beijing, CN;

Hua-Jun Zeng, Beijing, CN;

Jian Wang, Beijing, CN;

Assignee:

Microsoft Corporation, Redmond, WA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 15/18 (2006.01); G06E 1/00 (2006.01); G06E 3/00 (2006.01);
U.S. Cl.
CPC ...
Abstract

Embodiments of the invention relate to improvements to the support vector machine (SVM) classification model. When text data is significantly unbalanced (i.e., positive and negative labeled data are in disproportion), the classification quality of standard SVM deteriorates. Embodiments of the invention are directed to a weighted proximal SVM (WPSVM) model that achieves substantially the same accuracy as the traditional SVM model while requiring significantly less computational time. A weighted proximal SVM (WPSVM) model in accordance with embodiments of the invention may include a weight for each training error and a method for estimating the weights, which automatically solves the unbalanced data problem. And, instead of solving the optimization problem via the KKT (Karush-Kuhn-Tucker) conditions and the Sherman-Morrison-Woodbury formula, embodiments of the invention use an iterative algorithm to solve an unconstrained optimization problem, which makes WPSVM suitable for classifying relatively high dimensional data.


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