NESC9967 RECOG-ORNL.
RECOG-ORNL, Pattern Recognition Data Analysis
NAME OR DESIGNATION OF PROGRAM, COMPUTER, DESCRIPTION OF PROGRAM OR FUNCTION, METHOD OF SOLUTION, RESTRICTIONS ON THE COMPLEXITY OF THE PROBLEM, TYPICAL RUNNING TIME, UNUSUAL FEATURES OF THE PROGRAM, AUXILIARIES, STATUS, REFERENCES, REQUIREMENTS, LANGUAGE, OPERATING SYSTEM UNDER WHICH PROGRAM IS EXECUTED, OTHER PROGRAMMING OR OPERATING INFORMATION OR RESTRICTIONS, NAME AND ESTABLISHMENT OF AUTHORS, MATERIAL, CATEGORIES
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| Program name | Package id | Status | Status date |
|---|---|---|---|
| RECOG-ORNL | NESC9967/01 | Tested | 25-FEB-1993 |
| RECOG-ORNL | NESC9967/02 | Tested | 09-DEC-2000 |
Machines used:
| Package ID | Orig. computer | Test computer |
|---|---|---|
| NESC9967/01 | IBM 360 series | IBM 3090 |
| NESC9967/02 | VAX under VMS | VAX under VMS |
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3. DESCRIPTION OF PROGRAM OR FUNCTION
RECOG-ORNL, a general-purpose pattern recognition code, is a modification of the RECOG program, written at Lawrence Livermore National Laboratory. RECOG-ORNL contains techniques for preprocessing, analyzing, and displaying data, and for unsupervised and supervised learning. Data preprocessing routines transform the data into useful representations by autocalling, selecting important variables, and/or adding products or transformations of the variables of the data set. Data analysis routines use correlations to evaluate the data and interrelationships among the data. Display routines plot the multidimensional patterns in two dimensions or plot histograms, patterns, or one variable versus another. Unsupervised learning techniques search for classes contained inherently in the data. Supervised learning techniques use known information about some of the data to generate predicted properties for an unknown set.
RECOG-ORNL, a general-purpose pattern recognition code, is a modification of the RECOG program, written at Lawrence Livermore National Laboratory. RECOG-ORNL contains techniques for preprocessing, analyzing, and displaying data, and for unsupervised and supervised learning. Data preprocessing routines transform the data into useful representations by autocalling, selecting important variables, and/or adding products or transformations of the variables of the data set. Data analysis routines use correlations to evaluate the data and interrelationships among the data. Display routines plot the multidimensional patterns in two dimensions or plot histograms, patterns, or one variable versus another. Unsupervised learning techniques search for classes contained inherently in the data. Supervised learning techniques use known information about some of the data to generate predicted properties for an unknown set.
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NESC9967/01
NEA-DB ran the test case included in this package on an IBM 3090 computer in 4 seconds of CPU time.[ top ]
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| Package ID | Status date | Status |
|---|---|---|
| NESC9967/01 | 25-FEB-1993 | Tested at NEADB |
| NESC9967/02 | 09-DEC-2000 | Tested at NEADB |
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10. REFERENCES
- L.A. Cox, Jr., R.H. Pritchard, and C.F. Bender,
RECOG: A Polyalgorithm for the Analysis of Generalized Data Sets
An Operator' s Manual,
UCID-16433 Rev. 1 (April 9, 1975).
- L.A. Cox, Jr., R.H. Pritchard, and C.F. Bender,
RECOG: A Polyalgorithm for the Analysis of Generalized Data Sets
An Operator' s Manual,
UCID-16433 Rev. 1 (April 9, 1975).
NESC9967/01, included references:
- C.L. Begovich, and N.M. Larson:A User' s Manual for the Pattern Recognition Code RECOG-ONRL
ORNL/CSD/TM-21 (May 1977).
NESC9967/02, included references:
- C.L. Begovich, and N.M. Larson:A User' s Manual for the Pattern Recognition Code RECOG-ONRL
ORNL/CSD/TM-21 (May 1977).
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NESC9967/01
To run the test case on an IBM 3090 computer 580K bytes of main storage were required,[ top ]
| Package ID | Computer language |
|---|---|
| NESC9967/01 | FORTRAN-IV |
| NESC9967/02 | FORTRAN-77 |
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NESC9967/02
RECOGH2N.FOR RECOG-ORNL fortran sourceRERCOG.INP Sample problem input data
RECOG-IBM.OUT Sample problem output obtained on IBM 360
RECOG-VAX.OUT Sample problem output obtained on VAX
RECOG.INF Information file
NESC9967/01
| File name | File description | Records |
|---|---|---|
| NESC9967_01.001 | Information file | 55 |
| NESC9967_01.002 | JCL and control information | 117 |
| NESC9967_01.003 | RECOG-ORNL source - MAIN program | 123 |
| NESC9967_01.004 | RECOG-ORNL source - subroutine | 5807 |
| NESC9967_01.005 | Differences between original and NEADB vers. | 449 |
| NESC9967_01.006 | Sample problem input data | 319 |
| NESC9967_01.007 | Sample problem output | 1993 |
Keywords: computer graphics, correlations, data analysis, data processing, learning, pattern recognition.