Weighted parzen windows for pattern classification

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 18, Issue 5, 1996, Pages 567-574

  Babich, Gregory A ^a   Camps, Octavia I ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Bayes error; Clustering; Discriminant analysis; Holdout; Kernel estimator; Leave one out; Nonparametric classifiers; Parzen windows; Training samples

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; DATA STRUCTURES; ERRORS; LEARNING SYSTEMS; PARAMETER ESTIMATION; PATTERN RECOGNITION SYSTEMS;

BAYES ERROR; CLUSTERING; DISCRIMINANT ANALYSIS; ERROR RATE DEGRADATION; KERNEL ESTIMATOR; NONPARAMETRIC CLASSIFIERS; PATTERN CLASSIFICATION; TRAINING SAMPLES; WEIGHTED PARZEN WINDOWS;

NEURAL NETWORKS;

EID: 0030145852     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.494647     Document Type: Article

References (28)

1. R.P. Lippmann, "An Introduction to Computing with Neural Nets," IEEE ASSP Magazine, pp. 4-22, Apr. 1987.
2. R-P- Lippmann, "Pattern Classification Using Neural Networks," IEEE Communications Magazine, pp. 47-64, Nov. 1989.
3. S.J. Raudys and A.K. Jain,'"Small Sample Size Effects in Statistical Pattern Recognition; Recommendations -for Practitioners," IEEE Trans. Pattern Analysis und Machine Intelligence, vol. 13, pp. 252-264, 1991.
4. A.K. Jain and B. Chandrase'karan, "Dimensionality and Sample Size Considerations in Pattern Recognition Practice," Handbook of Statistics 2, P.R. Krishnaiah and L.N. Kanal, eds., pp. 835-855. North Holland, 1982.
5. S. Raudys and A.K. Jain, "Small Sample Size Problems in Designing Artificial Neural Networks," Artificial Neural Netivorks and Statistical Pattern Recognition Old and New Connections, I.K. Sethi and A.K. Jain, eds., pp. 33-50. Elseviei Science Publishers, 1991.
6. S. Raudys, "On Dimensionality, Learning Sample Size and Complexity of Classification Algorithms," Proc. Third Int'l Conf. Pattern Recognition, pp. 166-169,1976.
7. K. Fukunaga, Introduction to Statistical Pattern Recognition, second edition. Academic Press, 1990.
8. L. Kanal and B. Chandrasekaran, "On Dimensionality and Sample Size in Statistical Pattern Recognition," Pattern Recognition, pp. 238255,1971.
9. D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Learning Internal Representations by Error Propagation," Parallel Distributed Processing: Expirations in the Mierostructure of Cognition, chap. 8. MIT Press, 1986.
10. A.K. Jain and M.D. Ramaswami, "Classifier Design with Parzen Windows," Pattern Recognition and Artificial Intelligence, E.S, Gelsema and L.N. Kanal, eds., pp. 211-228. Elscvicr Science Publish ers, 1988.
11. K. Fukunaga and D.M. Hummels, "Bayes Error Estimation Using Parzen and JNN Procedures," IF.P.F. Trans. Pattern Analysis and Machine Intelligence, vol. 9, pp. 634-643,1987.
12. PA. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach. Prentice Hall, 1982.
13. W.Y. Huang and R.P. Lippmann, "Comparisons Between Neural Net and Conventional Classifiers," Proc. IEEE Int'l Conf. Neural Networks, pp. 1V4S5-1V493,1987.
14. A, Khotanzad and J.-H. Lu, "Distortion.Invariant Character Recognition by a Multi-Layer Perceptron -and Back-Propagation Learning," Proc-. IEEE Int'l Cor.f: Neurd"N.etworks,..Tpp. 1625-1632, 1988.
15. J. Van Ness, ."On the Dominance of NonTP-arametric Payes Rule Discriminant Algorithms in High Dimensions,"-Pattern Recognition, vol. 12, pp. 355-368,1980.
16. K. Fukurtaga and D.M. Hummels, '-'Bias of Nearest' Neighbor Estimates," IEEE Trans. Puttern Analysis and Machine 'Intelligence, vol. 9, pp.. 103-112,1987.
17. K. Fukunaga and K.K. Hayes.. "tsfiects'-'of- Sample Size in Classifier Design," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 8, pp. 873-885, Aug. 1989.
18. R.O. Duda and P.E. tiart,-Pattern Classification and Scene Analysis. John Wileyfe Sons, 1973.
19. A. Khotanzad and J.-H. Lu, "Shape and Texture Recognition by a Neural Network," Artificial Neural Networks ana Statistical Pattern, Recognition Old and New Connections, I.K. Sethi and A.K. Jain, eds:, pp. 109-131. Elsevier Science Publishers, 1991.
20. S. Amari, N. Fujita, and S. Shinomoto, "Four Types of Learning Curves,"' Neural Computation, vol. 4, pp. 605-618.. 1992.
21. E.B. Baum, "When Are -Nearest Neighbor' and Back Propagation Accurate for Feasible Sized Sets of Examples?"- Proc. EURASP Workshop Neural Networks, T.B. Alrrwida-afic G.J. "WeHekens,eds.', Delft' Univ. of Technology, pp. 2-25,1990.
22. D. Cohn and G. Tesauro, "How-Tight are the Vapnik-Chervonenkis Bounds?" Neural Computation, v ci 4, pp,-249-269,1992.
23. S. Raudys, "Why do Multilayer. -Perceotrons Have Favorable Small Sample Properties?" Pattern Recognition in Practice 4, E.S. Gelsema and L.N. Kanal,-eds.,-pp.. 2"87-298 Elsevier Science'B.V., 1994.
24. R.P.W. Duin, "Superlearaing Capabilities of Neural Networks,". Proc. Eighth Scandinavian Conf. Image Analysis, Tromso, pp. 547-554,1993.
25. S. Raudys, "On the Effectiveness of Parzen Window; Classifier," Information, vol. 2, no. 3, pp. 434-454,1991.
26. S. Raudys, "On Shape of Pattern Error Function. Initializations and Intrinsic Dimensionality in' ANN Classifier Design," Informatica, vol. 4, no. 3-4, pp. 360'-383, '1993.
27. S. Raudys, "Unexpected Small Sample Properties of the Linear Perceptrons," Technical report on research activities in LAFORIAIBP, Univ. PARIS VI, LAEORIA 95/17,. 1995.
28. G.F. Hughes, "On the Mean Accuracy-.of: Statistical'Pattern Recognizers," IEEE Trans. Information Theory,-vol. 14;' pp. 55-63, 1968.