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John Clare Brocklebank, 37Spring Hill, TN

Known as:
John C Brocklebank Jay C Brocklebank
Related to:
Nancy R Thornton 65 Kody Kurowski 37 Jeffrey L Berry 55 Kenneth J Kurowski 66 Michael Bickly 37

John Brocklebank Phones & Addresses

Spring Hill, TN   

Franklin, TN   

Punta Gorda, FL   

Nashville, TN   

5400 Hunter Hollow Dr, Raleigh, NC 27606    919-8513086   

Ocean Isle Beach, NC   

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John Brocklebank resumes & CV records

Resumes

John Brocklebank Photo 26

Dean, College Of Computing And Technology

Location:
5400 Hunter Hollow Dr, Raleigh, NC 27606
Industry:
Computer Software
Work:
Lipscomb University
Dean, College of Computing and Technology Sas
Executive Vice President and Chief Hosting Officer, Global Hosting and Us Professional Services Sas 2014 - Jan 2018
Senior Vice President, Sas Solutions Ondemand North Carolina State University 1999 - 2016
Adjunct Professor of Statistics and Genetics Sas 2008 - 2014
Vice President, Sas Solutions Ondemand Sas 1999 - 2008
Senior Research and Development Director Sas 1997 - 1999
Product Development Director, Data Mining Sas 1988 - 1996
Manager, Statistical Training and Services Sas 1983 - 1988
Manager, Statistical Training Sas 1981 - 1983
Statistical Training Specialist
Education:
North Carolina State University 1981 - 1981
Doctorates, Doctor of Philosophy, Mathematics, Statistics, Philosophy Vanderbilt University 1976 - 1976
Masters Lipscomb University 1970 - 1974
Bachelors, Mathematics
Skills:
Analytics, Business Intelligence, Enterprise Software, Sas, Saas, Business Analytics, Solution Selling, Predictive Analytics, Cloud Computing, Strategy, Data Mining, Professional Services, Statistics, Crm, Data Warehousing, Strategic Partnerships, Statistical Programming, Program Management, It Strategy, Time Series Analysis, Design of Experiments, Pre Sales, Salesforce.com, Project Management
Languages:
English
John Brocklebank Photo 27

John Brocklebank

John Brocklebank Photo 28

John Brocklebank

John Brocklebank Photo 29

John Brocklebank

Location:
United States

Publications & IP owners

Us Patents

Hybrid Neural Network Generation System And Method

US Patent:
6941289, Sep 6, 2005
Filed:
Apr 6, 2001
Appl. No.:
09/828290
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:
G06E001/00
G06E003/00
G06F015/18
G06G007/00
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.

Method For Selecting Node Variables In A Binary Decision Tree Structure

US Patent:
7127466, Oct 24, 2006
Filed:
Mar 10, 2003
Appl. No.:
10/384841
Inventors:
John C. Brocklebank - Raleigh NC, US
Bruce S. Weir - Raleigh NC, US
Wendy Czika - Raleigh NC, US
Assignee:
SAS Institute Inc. - Cary NC
International Classification:
G06F 17/00
US Classification:
707101, 707 2, 707 5, 707100, 707102
Abstract:
A method for selecting node variables in a binary decision tree structure is provided. The binary decision tree is formed by mapping node variables to known outcome variables. The method calculates a statistical measure of the significance of each input variable in an input data set and then selects an appropriate node variable on which to base the structure of the binary decision tree using an averaged statistical measure of the input variable and any co-linear input variables of the data set.

Hybrid Neural Network Generation System And Method

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.

Computer-Implemented Regression Systems And Methods For Time Series Data Analysis

US Patent:
7171340, Jan 30, 2007
Filed:
May 2, 2005
Appl. No.:
11/119670
Inventors:
John C. Brocklebank - Raleigh NC, US
Assignee:
SAS Institute Inc. - Cary NC
International Classification:
G06F 3/01
US Classification:
702189, 702183, 702190, 702196
Abstract:
Computer-implemented systems and methods for analyzing time series data. Statistical techniques are performed upon candidate autoregressive components and regressor components using the time series data. Autoregressive and regressor components are included in a predictive model based upon the autoregressive and regressor components' significance levels as determined by the statistical techniques.

Hybrid Neural Network Generation System And Method

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.

Computer-Implemented System And Method For Web Activity Assessment

US Patent:
7634423, Dec 15, 2009
Filed:
Aug 30, 2002
Appl. No.:
10/232153
Inventors:
John C. Brocklebank - Raleigh NC, US
Assignee:
SAS Institute Inc. - Cary NC
International Classification:
G06F 17/18
US Classification:
705 10, 707100
Abstract:
A computer-implemented system and method for evaluating customer activity. Data about the customer activity is received and is used to generate actual data values associated with preselected business metrics. One or more business metric score cards may be generated to assess how the business metrics are performing as well as what business metrics can be changed to better meet business goals.

Method For Selecting Node Variables In A Binary Decision Tree Structure

US Patent:
7809539, Oct 5, 2010
Filed:
Dec 6, 2002
Appl. No.:
10/313569
Inventors:
John C. Brocklebank - Raleigh NC, US
Bruce S. Weir - Seattle WA, US
Wendy Czika - Cary NC, US
Assignee:
SAS Institute Inc. - Cary NC
International Classification:
G06G 7/48
G06F 19/00
C12Q 1/00
US Classification:
703 11, 702 20, 435 4
Abstract:
A method for selecting node variables in a binary decision tree structure is provided. The binary decision tree is formed by mapping node variables to known outcome variables. The method calculates a statistical measure of the significance of each input variable in an input data set and then selects an appropriate node variable on which to base the structure of the binary decision tree using an averaged statistical measure of the input variable and any co-linear input variables of the data set.

Computer-Implemented System And Method For Web Activity Assessment

US Patent:
8000994, Aug 16, 2011
Filed:
Nov 5, 2009
Appl. No.:
12/613216
Inventors:
John C. Brocklebank - Raleigh NC, US
Assignee:
SAS Institute Inc. - Cary NC
International Classification:
G06F 17/18
G06Q 30/00
US Classification:
705 731, 705 729, 705 739
Abstract:
A computer-implemented system and method for evaluating customer activity. Data about the customer activity is received and is used to generate actual data values associated with preselected business metrics. One or more business metric score cards may be generated to assess how the business metrics are performing as well as what business metrics can be changed to better meet business goals.

Isbn (Books And Publications)

Sas For Forecasting Time Series

Author:
John C. Brocklebank
ISBN #:
0471395668

Possible Relatives

Donna Brocklebank  Douglas Brocklebank  Kevin R Brocklebank  Paul W Brocklebank  John M Johnson  John Stamps  John R Stan  John Stanard  John R Stanaway  John R Stanbery 

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