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.

Date of Patent:
Apr. 28, 2020

Filed:

Sep. 30, 2019
Applicant:

Sas Institute Inc., Cary, NC (US);

Inventors:

Xu Chen, Apex, NC (US);

Yingjian Wang, Apex, NC (US);

Saratendu Sethi, Raleigh, NC (US);

Assignee:

SAS Institute Inc., Cary, NC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/00 (2006.01); G06K 9/62 (2006.01); G06F 17/18 (2006.01); G06N 20/10 (2019.01); G06F 17/16 (2006.01);
U.S. Cl.
CPC ...
G06K 9/628 (2013.01); G06F 17/16 (2013.01); G06F 17/18 (2013.01); G06K 9/6256 (2013.01); G06K 9/6269 (2013.01); G06N 5/003 (2013.01); G06N 20/10 (2019.01);
Abstract

A computer trains a classification model. (A) An estimation vector is computed for each observation vector using a weight value, a mean vector, and a covariance matrix. The estimation vector includes a probability value for each class of a plurality of classes for each observation vector that indicates a likelihood that each observation vector is associated with each class. A subset of the plurality of observation vectors has a predefined class assignment. (B) The weight value is updated using the computed estimation vector. (C) The mean vector for each class is updated using the computed estimation vector. (D) The covariance matrix for each class is updated using the computed estimation vector. (E) A convergence parameter value is computed. (F) A classification model is trained by repeating (A) to (E) until the computed convergence parameter value indicates the mean vector for each class of the plurality of classes is converged.


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