Nonparametric classification with polynomial MPMC Cascades

Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Volumn , Issue , 2004, Pages 105-112

  Bohte, Sander M ^a,b   Breitenbach, Markus ^b   Grudic, Gregory Z ^b  

^a CWI   (Netherlands)

^b UCB 450   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; COMPUTER SCIENCE; CORRELATION METHODS; ERROR ANALYSIS; ERRORS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; POLYNOMIALS; RANDOM PROCESSES; REGRESSION ANALYSIS;

LEARNING COMPLEXITY; MINIMAX PROBABILITY MACHINE CLASSIFICATION (MPMC); NONPARAMETRIC ALGORITHMS; SUPPORT VECTOR MACHINES (SVM);

BINARY SEQUENCES;

EID: 14344265726     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Anderson, T. W., & Bahadur, R. R. (1962). Classification into two multivariate normal distributions with different covariance matrices. Annals of Mathematical Statistics, 33, 420-431.
2. Breiman, L. (1998). Arcing classifiers. Annals of Statistics, 26, 801-849.
3. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
4. Chang, C.-C., & Lin, C.-J. (2003). libSVM. v2.5 http://www.csie.ntu. edu.tw/~cjlin/libsvm/.
5. Fahlman, S., &: Lebiere, C. (1990). The cascade-correlation learning architecture. Advances in Neural Information Processing Systems (pp. 524-532). Denver 1989: Morgan Kaufmann, San Mateo.
6. Freund, Y. (1999). An adaptive version of the boost by majority algorithm. COLT: Proceedings of the Workshop on Computational Learning Theory, Morgan Kaufmann Publishers.
7. Friedman, J. (1995). An overview of computational learning and function approximation. From Statistics to Neural Networks, Proc. NATO/ASI Workshop. Springer-Verlag.
8. Gallant, S. (1990). Perceptron-based learning algorithms. IEEE Trans. on Neural Networks, 1, 179-191.
9. Grudic, G., & Lawrence, P. (1997). Is nonparametric learning practical in very high dimensional spaces? Proc. 15th Intern. Joint Conf. on AI (IJCAI-97) (pp. 804-809). San Francisco: Morgan Kaufmann Publishers.
10. Lanckriet, G., Ghaoui, L. E., Bhattacharyya, C., & Jordan, M. (2002). A robust minimax approach to classification. Journal of Machine Learning Research, 3, 555-582.
11. Nadal, J.-P. (1989). Study of a growth algorithm for a feed-forward network. International J. of Neural Systems, 1, 55-59.
12. Pena, D., & Prieto, F. (2001). Multivariate outlier detection and robust covariance matrix estimation. Technometrics, 43, 286-310.
13. Schölkopf, B., & Smola, A. (2002). Learning with kernels. Cambridge, MA: MIT Press.