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United States Patent 10,402,685
Guyon , et al. September 3, 2019
Recursive feature elimination method using support vector machines
Abstract
Identification of a determinative subset of features from within a group of features is
performed by training a support vector machine
using training samples with class
labels to determine a value of each feature,
where features are removed based on their
the value. One or more features having the
smallest values are removed and an updated
kernel matrix is generated using the remaining
features. The process is repeated until a
predetermined number of features remain which
are capable of accurately separating the data
into different classes. In some embodiments,
features are eliminated by a ranking criterion
based on a Lagrange multiplier corresponding to
each training sample.
Inventors: Guyon; Isabelle (Berkeley, CA), Weston; Jason Aaron Edward (New York, NY)
Applicant:
Name City State Country Type
Guyon; Isabelle
Weston; Jason Aaron Edward
Berkeley
New York
CA
NY
US
US
Assignee: HEALTH DISCOVERY CORPORATION (Atlanta, GA)
Family ID: 43781396
Appl. No.: 12/944,197
Filed: November 11, 2010
Prior Publication Data
Document Identifier Publication Date
US 20110106735 A1 May 5, 2011
Related U.S. Patent Documents
Application Number Filing Date Patent Number Issue Date
12890705 Sep 26, 2010 7970718
11928784 Sep 28, 2010 7805388
10494876 Jan 6, 2009 7475048
PCT/US02/35576 Nov 7, 2002
PCT/US02/16012 Jan 8, 2008 7318051
10057849 Oct 3, 2006 7117188
09633410 Apr 19, 2005 6882990
60347562 Nov 7, 2001
60292133 May 18, 2001
60292221 May 18, 2001
60332021 Nov 21, 2001
60161806 Oct 27, 1999
60168703 Dec 2, 1999
60184596 Feb 24, 2000
60191219 Mar 22, 2000
Current U.S. Class: 1/1
Current CPC Class: G06N 20/10 (20190101); G06N 20/00 (20190101); G06K 9/6231 (20130101); G16B 40/00 (20190201); G16B 25/00 (20190201)
Current International Class: G06N 20/00 (20190101); G06K 9/62 (20060101); G16B 40/00 (20190101); G06N 20/10 (20190101); G16B 25/00 (20190101)
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