Online algorithms for modeling distributions using examples
| Autor: | M.T. Arvind, M.A.L. Thathachar |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Adaptive algorithm
Computer science business.industry Machine learning computer.software_genre Tracking (particle physics) Measure (mathematics) Distribution (mathematics) Information engineering Convergence (routing) Algorithm design Artificial intelligence Online algorithm business computer Algorithm |
| Zdroj: | Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat. No.97TH8237). |
| DOI: | 10.1109/icics.1997.652201 |
| Popis: | This paper addresses the problem of modeling the relationships between observed samples of data as a distribution. An L-step dependent model is constructed and an online algorithm is designed for the model in order to minimize the Kullback measure. The algorithm is analyzed to show that it converges weakly to global optimum of Kullback measure for that model. Simulation studies indicate that the algorithm has better tracking properties for time-varying distributions, when compared with statistical estimation procedures. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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