Energy minimization for classification, pattern recognition,...

Image analysis – Pattern recognition

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C382S133000, C382S159000, C382S170000, C382S224000, C382S280000, C382S281000, C706S020000

Reexamination Certificate

active

06993186

ABSTRACT:
An analyzer/classifier tool (100) for data comprises use of an energy minimization process (120) with data transformed by an input process (110) into one or more input matrices. The data to be analyzed/classified is processed by an energy minimization technique such as individual differences multidimensional scaling (IDMDS) to produce at least a rate of change of stress/energy. Using the rate of change of stress/energy and possibly other IDMDS output, a back end process (130) analyzes and classifies data through patterns recognized within the data.

REFERENCES:
patent: 5175710 (1992-12-01), Hutson
patent: 5181259 (1993-01-01), Rorvig
patent: 5235506 (1993-08-01), O'Brien, Jr.
patent: 5245587 (1993-09-01), Hutson
patent: 5321613 (1994-06-01), Porter et al.
patent: 5348020 (1994-09-01), Hutson
patent: 5402335 (1995-03-01), O'Brien
patent: 5422961 (1995-06-01), Simard
patent: 5437279 (1995-08-01), Gray
patent: 5490516 (1996-02-01), Hutson
patent: 5574837 (1996-11-01), Clark et al.
patent: 5579766 (1996-12-01), Gray
patent: 5596644 (1997-01-01), Abel et al.
patent: 5602938 (1997-02-01), Akiyama et al.
patent: 5625767 (1997-04-01), Bartell et al.
patent: 5706402 (1998-01-01), Bell
patent: 5729451 (1998-03-01), Gibbs et al.
patent: 5802207 (1998-09-01), Huang
patent: 5987094 (1999-11-01), Clarke et al.
patent: 6332034 (2001-12-01), Makram-Ebeid et al.
patent: 6546117 (2003-04-01), Sun et al.
Article titled “On The Theory of Scales of Measurement”, Science, vol. 103, No. 2684, dated Jun. 7, 1946.
AGW Consulting, Inc., “Final Report Emergent Pattern Recognition Analysis of Simulated SCADA System Data”, for New Mexico State University, dated Dec. 15, 1998.
Susan S. Schiffman et al., article titled “Treating Rectangular Matrices By Multidimensional Scaling”, Introduction To Multidimensional Scaling, Academic Press, Orlando, 1981, pp 321 to 331.
Malcolm P. Young, article entitled “The Organization of Neural Systems In The Primate Cerebral Cortex”, Biological Sciences, vol. 252, Issue 1333, Apr. 1993. pp. 12 to 18.
Geoffrey J. Goodhill et al., article entitled “An Evaluation Of The Use Of Multidimensional Scaling For Understanding Brain Connectivity”, University of Edinburgh, UK, Jun. 1994, pp. 1 to 23.
McGee, Victor E., “The Multidimensional Analysis of ‘Elastic’ Distances”,The British Journal of Mathematical and Statistical Psychology, vol. 19, Part 2, pp. 181-196, Nov., 1966.
McGee, Victor E., “Multidimensional Scaling of N Sets of Similarity Measures: A Nonmetric Individual Differences Approach”,Multivariate Behavioral Research, Apr. 1968, 3, pp. 233-248, Apr., 1968.
Baird, John et al.,Fundamentals of Scaling and Psychophysics, Chapter 10, pp. 177-205. Published by John Wiley & Sons, Inc., 1978.
Sinha et al., “A General Class of Aggregation Operators with Applications to Information Fusion in Distributed Systems”, 1989 IEEE Intel Conference on Systems, Man and Cybernetics, (PROC), pp. 921-927, Nov. 14-17, 1989.
Zhou et al., “A Linearly Constrained Least Squares Approach for Multisensor Data Fusion”, SPIE, vol. 3067, pp. 118-129, Apr. 24-25, 1997.
Arabie, P. et al., Three-way Scaling and Clustering, Sage Publications, 1987, pp. 7-53.
Bosch, R. and Smith, J., Separating hyperplanes and the authorship of the disputed federalist papers, American Mathematical Monthly, vol. 105, Aug-Sep. 1998, pp. 601-608.
Carroll, J.D. and Chang, J.-J., “Analysis of individual differences in multidimensional scaling via an n-way generalization of the ‘Eckart-Young’ decomposition,” Psychometrika, vol. 35, No. 3, Sep., 1970, pp. 283-319.
Commandeur, J. and Heiser, W., “Mathematical derivations in the proximity scaling (PROXSCAL) of symmetric data matrices,” Tech. report No. RR-93-04, Department of Data Theory, Leiden University, Leiden, 1993, pp. 1-72.
de Leeuw, J. and Heiser, W., “Theory of multidimensional scaling,” in P.R. Krishnaiah and L.N. Kanal, eds., Handbook of Statistics, vol. 2, North-Holland Pub. Co., New York, 1982, pp. 285-316.
McGee, V.E., “The multidimensional analysis of ‘elastic’ distances,” The British Journal of Mathematical and Statistical Psychology, vol. 19, part 2, Nov., 1966, pp. 181-196.
McGee, V.E., “Multidimensional Scaling of n sets of similarity measures: a nonmetric individual difference approach,” Multivariate Behavioral Research, Apr. 1968, pp. 233-249.
Takane, Y., Young, F., and deLeeuw, J., “Nonmetric individual differences multidimensional scaling: an alternating least squares method with optimal scaling features,” Psychometrika, vol. 42, No. 1, Mar. 1977, pp. 7-67.
Wish, M. and Carroll, J.D., “Multidimensional scaling and its applications,” in P.R. Krishnaiah and L.N. Kanal eds., Handbook of Statistics , vol. 2, North-Holland Pub. Co., New York, 1982, pp.317-345.
Young, M., “The organization of neural systems in the primate cerebral cortex,” Biological Sciences, Proceedings of the Royal Society, 1993, vol. 252, pp. 13-18.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Energy minimization for classification, pattern recognition,... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Energy minimization for classification, pattern recognition,..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Energy minimization for classification, pattern recognition,... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3574247

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.