James D Goodnight

US Patent:

7162461, Jan 9, 2007

Filed:

Sep 2, 2005

Appl. No.:

11/218970

Inventors:

James Howard Goodnight - Cary NC, US
Wolfgang Michael Hartmann - Heidelberg, DE
John C. Brocklebank - Raleigh NC, US

Assignee:

SAS Institute Inc. - Cary NC

International Classification:

G06E 3/00
G06E 1/00
G06F 15/18
G06G 7/00
G06N 3/02

US Classification:

706 15, 706 16, 706 19

Abstract:

A computer-implemented method and system for building a neural network is disclosed. The neural network predicts at least one target based upon predictor variables defined in a state space. First, an input data set is retrieved that includes the predictor variables and at least one target associated with the predictor variables for each observation. In the state space, a number of points is inserted in the state space based upon the values of the predictor variables. The number of points is less than the number of observations. A statistical measure is determined that describes a relationship between the observations and the inserted points. Weights and activation functions of the neural network are determined using the statistical measure.

US Patent:

7340440, Mar 4, 2008

Filed:

Dec 8, 2006

Appl. No.:

11/636322

Inventors:

James Howard Goodnight - Cary NC, US
Wolfgang Michael Hartmann - Heidelberg, DE
John C. Brocklebank - Raleigh NC, US

Assignee:

SAS Institute Inc. - Cary NC

International Classification:

G06F 15/18

US Classification:

706 19, 706 12, 706 14, 706 15

Abstract:

A computer-implemented method and system for building a neural network is disclosed. The neural network predicts at least one target based upon predictor variables defined in a state space. First, an input data set is retrieved that includes the predictor variables and at least one target associated with the predictor variables for each observation. In the state space, a number of points is inserted in the state space based upon the values of the predictor variables. The number of points is less than the number of observations. A statistical measure is determined that describes a relationship between the observations and the inserted points. Weights and activation functions of the neural network are determined using the statistical measure.

US Patent:

20030014378, Jan 16, 2003

Filed:

Apr 6, 2001

Appl. No.:

09/828290

Inventors:

James Goodnight - Cary NC, US
Wolfgang Hartmann - Heidelberg, DE
John Brocklebank - Raleigh NC, US

International Classification:

G06F015/18

US Classification:

706/021000, 706/026000

Abstract:

A computer-implemented method and system for building a neural network is disclosed. The neural network predicts at least one target based upon predictor variables defined in a state space. First, an input data set is retrieved that includes the predictor variables and at least one target associated with the predictor variables for each observation. In the state space, a number of points is inserted in the state space based upon the values of the predictor variables. The number of points is less than the number of observations. A statistical measure is determined that describes a relationship between the observations and the inserted points. Weights and activation functions of the neural network are determined using the statistical measure.

US Patent:

20110202329, Aug 18, 2011

Filed:

Feb 12, 2010

Appl. No.:

12/705204

Inventors:

James Howard Goodnight - Cary NC, US
Steve Krueger - Raleigh NC, US
Oliver Schabenberger - Cary NC, US
Christopher D. Bailey - Cary NC, US

International Classification:

G06F 15/76
G06F 9/06
G06F 9/00
G06F 15/16

US Classification:

703 21, 712 30, 718101, 709250, 712E09003

Abstract:

Systems and methods are provided for generating multiple system state projections for one or more scenarios using a grid computing environment. A central coordinator software component executes on a root data processor and provides commands and data to a plurality of node coordinator software components. A node coordinator software component manages threads which execute on its associated node data processor and which perform a set of matrix operations. Stochastic simulations use results of the matrix operations to generate multiple state projections. Additional processing can be performed by the grid computing environment based upon the generated state projections, such as to develop risk information for users.

US Patent:

20120159489, Jun 21, 2012

Filed:

Dec 20, 2010

Appl. No.:

12/972840

Inventors:

Oliver Schabenberger - Cary NC, US
James Howard Goodnight - Cary NC, US

International Classification:

G06F 9/46

US Classification:

718100

Abstract:

Systems and methods are provided for a data processing system having multiple executable threads that is configured to generate a cross-product matrix in a single pass through data to be analyzed. An example system comprises memory for receiving the data to be analyzed, a processor having a plurality of executable threads for executing code to analyze data, and software code for generating a cross-product matrix in a single pass through data to be analyzed. The software code includes threaded variable levelization code for generating a plurality of thread specific binary trees for a plurality of classification variables, variable tree merge code for combining a plurality of the thread-specific trees into a plurality of overall trees for the plurality of classification variables, effect levelization code for generating a plurality of sub-matrices of the cross-product matrix using the plurality of the overall trees for the plurality of classification variables, and cross-product matrix generation code for generating the cross-product matrix by storing and ordering the elements of the sub-matrices in contiguous memory space.