Correntropy in data classification

Springer Proceedings in Mathematics and Statistics

Volumn 20, Issue , 2012, Pages 81-117

  Syed, Mujahid N ^a   Principe, Jose C ^a   Pardalos, Panos M ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

Artificial neural networks; Convolution smoothing; Correntropy; Simulated annealing; Statisticalclassification; Support vector machines

Indexed keywords

CLASSIFICATION METHODS; CLASSIFICATION PERFORMANCE; CORRENTROPY; NONPARAMETRIC CLASSIFICATION; QUADRATIC LOSS FUNCTIONS; SIMULATED ANNEALING OPTIMIZATION; SOFT-MARGIN SUPPORT VECTOR MACHINES; STATISTICALCLASSIFICATION;

ALGORITHMS; CONVOLUTION; MONTE CARLO METHODS; NEURAL NETWORKS; SIMULATED ANNEALING; SUPPORT VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION);

EID: 84892720780     PISSN: 21941009     EISSN: 21941017     Source Type: Conference Proceeding    
DOI: 10.1007/978-1-4614-3906-6_5     Document Type: Article

References (37)

1. Alizamir, S., Rebennack, S., Pardalos, P.M.: Improving the neighborhood selection strategy in simulated annealing using the optimal stopping problem. In: Tan, C.M. (ed.) Simulated Annealing. Springer, New York, pp. 63-382 (2008)
2. Anthony, M., Bartlett, P.L.: Neural Network Learning: Theoretical Foundations. Cambridge University Press, UK (2009)
3. Antonov, G.E., Katkovnik, V.J.: Generalization of the concept of statistical gradient. Avtomat. i Vycisl. Tehn. (Riga) 4, 25-30 (1972)
4. Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programming: Theory And Algorithms. Wiley, New York (2006)
5. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144-152. ACM (1992)
6. Catoni, O.: Metropolis, simulated annealing and IET algorithms: Theory and experiments. J. Complex. 12, 595-623 (1996)
7. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273-297 (1995)
8. Fan, R.E., Chen, P.H., Lin, C.J.: Working set selection using second order information for training support vector machines. J. Mach. Learn. Res. 6, 1889-1918 (2005)
9. Gunn, S.R.: Support vector machines for classification and regression. ISIS technical report, 14 (1998)
10. Heisele, B., Ho, P., Poggio, T.: Face recognition with support vector machines: Global versus component-based approach. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. vol. 2, pp. 688-694. IEEE (2001)
11. Hornick, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359-366 (1989)
12. Kim, K.I., Jung, K., Park, S.H., Kim, H.J.: Support vector machines for texture classification. IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1542-1550 (2002)
13. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671 (1983)
14. Lundy, M., Mees, A.: Convergence of an annealing algorithm. Math. Progr. 34(1), 111-124 (1986)
15. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 5(4), 115-133 (1943)
16. Mehrotra, K., Mohan, C.K., Ranka, S.: Elements of Artificial Neural Networks. MIT Press, Cambridge (1997)
17. Michalewicz, Z., Fogel, D.B.: How to Solve It: Modern Heuristics. Springer, New York (2004)
18. Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York (1994)
19. Minsky, M., Seymour, P.: Perceptrons. MIT Press, Cambridge (1969)
20. Pardalos, P., Pitsoulis, L., Mavridou, T., Resende, M.: Parallel search for combinatorial optimization: Genetic algorithms, simulated annealing, tabu search and grasp. Parallel Algorithms for Irregularly Structured Problems, pp. 317-331. Wiley, Hoboken (1995)
21. Pardalos, P.M., Boginski, V.L., Vazacopoulos, A.: Data Mining in Biomedicine. Springer, New York (2007)
22. Principe, J.C.: Information Theoretic Learning: Renyi's Entropy And Kernel Perspectives. Springer, New York (2010)
23. Reeves, C.R.: Modern heuristic techniques for combinatorial problems. Wiley, New York (1993)
24. Robbins, H., Monro, S.L.: A stochastic approximation method. Ann. Math. Stat. 22, 400-407 (1951)
25. Rosenblatt, F.: The perceptron: A probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)
26. Rubinstein, R.Y.: Smoothed functionals in stochastic optimization. Math. Oper. Res., 26-33 (1983)
27. Santamaría, I., Pokharel, P.P., Principe, J.C.: Generalized correlation function: Definition, properties, and application to blind equalization. IEEE Trans. Signal Process. 54(6), 2187-2197 (2006)
28. Schölkopf, B., Burges, C., Vapnik, V.: Extracting support data for a given task. In: Proceedings, First International Conference on Knowledge Discovery & Data Mining, pp. 252-257. AAAI Press, Menlo Park (1995)
29. Singh, A., Principe, J.C.: A loss function for classification based on a robust similarity metric. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1-6. IEEE (2010).
30. Styblinski, M.A., Tang, T.S.: Experiments in nonconvex optimization: stochastic approximation with function smoothing and simulated annealing. Neural Netw. 3(4), 467-483 (1990)
31. Syed, M.N., Pardalos, P.M.: Neural network models in combinatorial optimization. Handbook of Combinatorial Optimization. In press
32. Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45-66 (2002)
33. Vapnik, V., Golowich, S.E., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing. Adv. Neural Inf. Process. Syst. 9, 281-287 (1996)
34. Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988-999 (1999)
35. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (2000)
36. Weston, J., Watkins, C.: Multi-class support vector machines. Technical report, Technical Report CSD-TR-98-04, Department of Computer Science, University of London, Royal Holloway (1998)
37. Zhang, J., Xanthopoulos, P., Chien, J., Tomaino, V., Pardalos, P.M.: Minimum prediction error models and causal relations between multiple time series. In: Cochran, J.J. (ed.) Wiley Encyclopedia of Operations Research and Management Science 3, 1843-1850 (2011)