A5 problem solving paradigm: A unified perspective and new results on RHT computing, mixture based learning, and evidence combination

2005 IEEE International Conference on Granular Computing

Volumn 2005, Issue , 2005, Pages 70-77

  Xu, Lei ^a,b  

^a IEEE   (Hong Kong)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

GRID COMPUTING; HOUGH TRANSFORM (HT); MODULAR STRUCTURES; OBJECT DETECTION;

COMPUTER SIMULATION; DATABASE SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; PROBLEM SOLVING;

DATA STRUCTURES;

EID: 33845296126     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/GRC.2005.1547237     Document Type: Conference Paper

References (24)

1. J.Illingworth and J.Kittler, A survey of the Hough Transform, Comput. Vision Graphics and Image Process. 43, 1988, 221-238.
2. P.V.C. Hough, "Method and means for recognizing complex patterns", U.S. Patent 3069654, Dec. 18, 1962.
3. X. L. Hu and L. Xu, "Investigation on Several Model Selection Criteria for Determining the Number of Cluster", Neural Information Processing - Letters and Reviews, Vol.4, No.1, July 2004, 2004, pp. 1-10.
4. X. L. Hu and L. Xu, "A Comparative Study of Several Cluster Number Selection Criteria", Lecture Notes in Computer Science, LNCS 2690, Proc. of IDEAL03, Springer-Verlag, 2003, pp195-202.
5. R.A. Jacobs, et al, "Adaptive mixtures of local experts", Neural Computation, 3, 1991, 79-87.
6. H. Kalviainen, P. Hirvonen, L. Xu, and E.Oja, "Probabilistic and Nonprobabilistic Hough Transforms: Overview and Comparisons", Image and Vision Computing, Vol.5, No. 4, 1995, pp239-252.
7. R.A. Redner & H.F. Walker, "Mixture densities, maximum likelihood, and the EM algorithm", SIAM Review, 26, 195-239, 1984.
8. V.N. Vapnik, The Nature Of Statistical Learning Theory, Springer-Verlag, 1995.
9. L. Xu, "Bayesian Ying Yang Learning ((II): A New Mechanism for Model Selection and Regularization", Intelligent Technologies for Information Analysis, N. Zhong and J. Liu (eds), S pringer, 2004, pp653-697.
10. L. Xu, "Data smoothing regularization, multi-sets-learning, and problem solving strategies", Neural Networks, Vol. 16, 2003, pp817-825.
11. L. Xu, "BYY Harmony Learning, Structural RPCL, and Topological Self-Organizing on Unsupervised and Supervised Mixture Models", Neural Networks, Vol.15, 2002, 1125-1151.
12. L. Xu, "Best Harmony, Unified RPCL and Automated Model Selection for Unsupervised and Supervised Learning on Gaussian Mixtures, MERBF Models and Three-Layer Nets", International Journal of Neural Systems, Vol.11, No.1, 2001, pp3-69.
13. L. Xu, "Rival Penalized Competitive Learning, Finite Mixture, and Multisets Clustering", Proc. of IEEE-INNS IJCNN98, Anchorage, Alaska, Vol.II, May 5-9, 1998, pp2525-2530.
14. L. Xu and M.I. Jordan, "On Convergence Properties of The EM Algorithm for Gaussian Mixtures", Neural Computation, 8, No.1, 1996, 129-151.
15. L. Xu, M.I.Jordan, and G.E.Hinton, "An Alternative Model for Mixtures of Experts", in Cowan, Tesauro, and Alspector, eds., Advances in Neural Information Processing Systems 7, MIT Press, pp.633-640.
16. L. Xu, "Bayesian-Kullback Coupled YING-YANG Machines: Unified Learnings and New Results on Vector Quantization", Proc. Intl. Conf. on Neural Information Processing (ICONIP96), Oct 30-Nov.3, 1995, Beijing, China, pp.977-988.
17. L. Xu, "A Unified Learning Framework: Multisets Modeling Learning", Invited Talk, Proc. of World Congress on Neural Networks (WCNN95), July 17-21, 1995, Washington, DC, Vol.1, pp.35-42.
18. L. Xu, "Multisets Modeling Learning: An Unified Theory for Supervised and Unsupervised Learning", Invited Talk, Proc. of IEEE ICNN94, June 26-July 2, 1994, Orlando, Florida, Vol.1, 315-320.
19. L. Xu, A. Krzyzak, and E.Oja, "Rival Penalized Competitive Learning for Clustering Analysis, RBF net and Curve Detection", IEEE Trans. on Neural Networks, Vol.4, No.4, 1993, pp636-649.
20. L. Xu, A. Krzyzak, and E.Oja, "Unsupervised and Supervised Classifications by Rival Penalized Competitive Learning", Proc. of 11th International Conference on Pattern Recognition (ICPR92), Hauge, Netherlands, Aug.30-Sept.3, 1992, Vol.I, pp.672-675.
21. L. Xu and M.I. Jordan, "Theoretical and experimental studies of the EM algorithm for unspervised learning based on finite Gaussian mixtures", MIT Computational Cognitive Science, Technical Report 9302, Dept. of Brain and Cognitive Science, MIT, Cambridge, MA, 1993.
22. L. Xu and E. Oja, "Randomized Hough Transform (RHT): Basic Mechanisms, Algorithms and Complexities", Computer Vision, Graphics, and Image Processing: Image Understanding, Vol.57, 1993, pp.131-154.
23. L. Xu, A. Krzyzak, and C.Y. Sun, "Several Methods for Combining Multiple Classifiers and Their Applications in Handwritten Character Recognition", IEEE Trans. on System, Man and Cybernetics, Vol. SMC-22, No.3, 1992, pp.418-435.
24. L. Xu, E. Oja, and P.Kultanen, "A New Curve Detection Method: Randomized Hough transform (RHT)", Pattern Recognition Letters, Vol.11, 1990, pp331-338.