Mary El Swift

US Patent:

20190026656, Jan 24, 2019

Filed:

Sep 25, 2018

Appl. No.:

16/141203

Inventors:

- Armonk NY, US
Stephan J. Roorda - Fuquay-Varina NC, US
Mary D. Swift - Rochester NY, US

International Classification:

G06N 99/00
G09B 7/00

Abstract:

A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighed differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.

US Patent:

20170046623, Feb 16, 2017

Filed:

Aug 13, 2015

Appl. No.:

14/825423

Inventors:

- Armonk NY, US
Stephan J. Roorda - Fuquay-Varina NC, US
Mary D. Swift - Rochester NY, US

International Classification:

G06N 99/00
G09B 7/00

Abstract:

A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighed differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.

US Patent:

20170046624, Feb 16, 2017

Filed:

Sep 22, 2015

Appl. No.:

14/861254

Inventors:

- Armonk NY, US
Stephan J. Roorda - Fuquay-Varina NC, US
Mary D. Swift - Rochester NY, US

International Classification:

G06N 99/00

Abstract:

A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighed differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.