Bayesian Approach to the Pattern Recognition Problem in Nonstationary Environment
The classical learning problem of the pattern recognition in a finite-dimensional linear space of real-valued features is studied under the conditions of a non-stationary universe. The training criterion of non-stationary pattern recognition is formulated as a generalization of the classical Support Vector Machine. The respective numerical algorithm has the computation complexity proportional to the length of the training time series.
- 1.Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)Google Scholar
- 2.Salganicoff, M.: Tolerating concept and sampling shift in lazy learning using prediction error context switching. AI Review, Special Issue on Lazzy Learning 11(1-5), 133–155 (1997)Google Scholar
- 3.Klinkenberg, R.: Learning drifting concepts example selection vs. example weighting. Intelligent data analysis, Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift 8(3) (2004)Google Scholar
- 6.Tatarchuk, A.I., Sulimova, V.V., Mottl, V.V., Windridge, D.: Method of relevant potential functions for selective combination of diverse information in the pattern recognition learning based on Bayesian approach. In: MMRO-14: Conf. Proc., Suzdal, pp. 188–191 (2009)Google Scholar
- 7.Ma, J., Saul, K.L., Savage, S., Voelker, G.: Identifying Suspicious URLs: An Application of Large-Scale Online Learning. In: Proceedings of the International Conference on Machine Learning (ICML), Montreal, Quebec, June 2009, pp. 681–688 (2009)Google Scholar