Data processing: artificial intelligence – Machine learning
Reexamination Certificate
2011-07-12
2011-07-12
Fernandez Rivas, Omar F (Department: 2129)
Data processing: artificial intelligence
Machine learning
Reexamination Certificate
active
07979363
ABSTRACT:
A system and method for estimating the a priori probability of a class-of-interest in an input-data-set and a system and method for evaluating the performance of the adaptive Bayes classifier in classifying unlabeled samples from an input-put-data-set. The adaptive Bayes classifier provides a capability to classify data into two classes, a class-of-interest or a class-other, with minimum classification error in an environment where a priori knowledge, through training samples or otherwise, is only available for a single class, the class-of-interest. This invention provides a method and system for estimating the a priori probability of the class-of-interest in the data set to be classified and evaluating adaptive Bayes classifier performance in classifying data into two classes, a class-of-interest and a class-other, using only labeled training samples, or otherwise, from the class-of-interest and unlabeled samples from the data set to be classified.
REFERENCES:
patent: 5335291 (1994-08-01), Kramer
patent: 5754681 (1998-05-01), Watanabe et al.
patent: 6095982 (2000-08-01), Richards-Kortum et al.
patent: 6418409 (2002-07-01), Metzger
patent: 6732083 (2004-05-01), Bax
Guerrero-Curieses et al. “Supervised Classification of Remote Sensing Images with Unknown Classes”, IEEE, 2000, pp. 3486-3488.
Mantero et al. “Partially Supervised Classification of Remote Sensing Images Through SVM-Based Probability Density Estimation”, IEEE GRS, vol. 43, No. 3, 2005, pp. 559-570 (cited in IDS).
T. Cacoullos, “Estimation of a multivariate density,” Ann. Inst. Statist. Math., vol. 18, pp. 179-189, 1966.
R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, New York: John Wiley & Sons, 1973.
E. Fix, et al “Discriminatory analysis, nonparametric discrimination,” USAF School of Aviation Medicine, Randolph Field, Tex Project 21-49-004, Rep. 4, 1951.
K. Fukunaga, et al, “Bayes Error Estimation Using Parzen and k-NN Procedures”, IEEE Transactions on PAMI, vol. PAMI-9, No. 5, Sep. 1987, p. 634-643.
B. Gorte, et al, “Non-parametric classification algorithm with an unknown class”, Proceedings of the International Symposium on Computer Vision, pp. 443-448, 1995.
A. Guerrero, et al., “Supervised Classification of Remote Sensing Images with Unknown Classes,” Proceedings of IGARSS-2002 Conference, Toronto, Canada, Jun. 2002.
J. Grim, et al., “Initialing Normal Mixtures of Densities,” Proceedings of the 14th International Conference on Pattern Recognition-vol. 1, p. 886, 1998.
I. L. Johnson, Jr., “The Davidon-Fletcher-Powell Penalty Function Method: A Generalized Iterative Technique for Solving parameter Optimization Problems”, NASA TN D-8251,1976.
M. C. Jones and D. A. Henderson, “Maximum likelihood kernel density estimation,” Technical Report May 2001, Depmt of Statistics., Open University, Jan. 2005.
P. Mantero, “Partially supervised classification of remote sensing images using SVM-based probability density estimation”, IEEE Trans Geo/Remote Sens.vol. 43, No. 3, Mar. 2005.
T. C. Minter, “A Discriminant Procedure for Target Recognition in Imagery Data”, Proceedings of the IEEE 1980 Nat. Aerospace and Electronic Conference—NAECON 1980, May 1980.
E. Parzen, “On estimation of a probability density function and mode,” Ann. Math. Statist., vol. 33, pp. 1065-1076, 1962.
M. Rosenblatt, “Remarks on some nonparametric estimates of a density function,” Ann. Math. Statist., vol. 27, pp. 832-837, 1956.
P. Whittle, “On the smoothing of probability density functions,” J. Roy. Statist., Ser B, vol. 20, pp. 334-343, 1958.
C. K. Chow, “On Optimum Recognition Error and Reject Tradeoff”, IEEE Trans. on info. Theory, vol. IT-16, No. 1, Jan. 1970, pp. 41-46.
K. Fukunaga, et. al., “Application of Optimum Error-Reject Functions,” IEEE Trans. on Infomation Theory, Nov. 1972, pp. 814-817.
K. Fukunaga, et. al., “Estimation of Classifier Performance,” IEEE Trans. on PAMI, vol. 11. No. 10, Oct. 1989, pp. 1087-1101.
K. Fukunaga, et. al., Nonparametric Bayes Error Estimation Using Unclassified Samples, IEEE Trans. on Infomation Theory, vol. IT-19, No. 4, Jul. 1973, pp. 434-440.
K. Fukunaga, et. al., “Leave-One-Out Procedures for Nonparametric Error Estimates,” IEEE Trans. on PAMI, vol. II. No. 4, Apr. 1989, pp. 421-423.
A. K. Jain, “Biometrics: A Grand Challenge”, Proceeding of the 17th International Conference on Pattern Recognition, (ICPR'04), 2004.
NIST Report to Congress , “Summary of NIST Standards for Biometric Accuracy, Tamper Resistance, and Interoperability.”, Nov. 2002.
B. Eckstein, “Evaluating the Benefits of assisted Target Recognition”, Proceeding of the 30th Applied Imagery Pattern recognition Workshop (AIPR'01) , 2001.
S. Rizvi, “Fusion Techniques for Automatic Target Recognition”, Proceedings of the 32nd Applied Imagery Pattern Recognition Workshop (AIPR'03), 2003.
B. Jeon and D. A. Landgrebe, “Partially Supervised Classification With Optimal Significance Testing,” Geoscience and Remote Sensing Symposium, 1993, pp. 1370-1372.
A. K. Jain, et. al., “Statistical Pattern Recognition: A Review”, IEEE Trans. on PAMI, vol. 22, No. 1, pp. 4-37, Jan. 2000.
Chang Li-Wu
Fernandez Rivas Omar F
LandOfFree
Priori probability and probability of error estimation for... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Priori probability and probability of error estimation for..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Priori probability and probability of error estimation for... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2672980