Stephen J Verzi

US Patent:

8166051, Apr 24, 2012

Filed:

Feb 3, 2009

Appl. No.:

12/364753

Inventors:

Travis L. Bauer - Albuquerque NM, US
Zachary O. Benz - Albuquerque NM, US
Stephen J. Verzi - Albuquerque NM, US

Assignee:

Sandia Corporation - Albuquerque NM

International Classification:

G06F 17/30
G06F 17/00

US Classification:

707750, 707749

Abstract:

An improved entropy-based term dominance metric useful for characterizing a corpus of text documents, and is useful for comparing the term dominance metrics of a first corpus of documents to a second corpus having a different number of documents.

US Patent:

20190392301, Dec 26, 2019

Filed:

Jun 20, 2018

Appl. No.:

16/013810

Inventors:

- Albuquerque NM, US
William Mark Severa - Albuquerque NM, US
James Bradley Aimone - Albuquerque NM, US
Stephen Joseph Verzi - Albuquerque NM, US

International Classification:

G06N 3/08
G06K 9/62
G06F 15/18

Abstract:

A method for increasing a speed and efficiency of a computer when performing machine learning using spiking neural networks. The method includes computer-implemented operations; that is, operations that are solely executed on a computer. The method includes receiving, in a spiking neural network, a plurality of input values upon which a machine learning algorithm is based. The method also includes correlating, for each input value, a corresponding response speed of a corresponding neuron to a corresponding equivalence relationship between the input value to a corresponding latency of the corresponding neuron. Neurons that trigger faster than other neurons represent close relationships between input values and neuron latencies. Latencies of the neurons represent data points used in performing the machine learning. A plurality of equivalence relationships are formed as a result of correlating. The method includes performing the machine learning using the plurality of equivalence relationships.