Abstract
When there are multiple component predictors, it is promising to integrate them into one predictor for advanced reasoning. If each component predictor is given as a stochastic model in the form of probability distribution, an exponential mixture of the component probability distributions provides a good way to integrate them. However, weight parameters used in the exponential mixture model are difficult to estimate if there is no data for performance evaluation. As a suboptimal way to solve this problem, weight parameters may be estimated so that the exponential mixture model should be a balance point that is defined as an equilibrium point with respect to the distance from/to all component probability distributions. In this paper, we propose a weight parameter estimation method that represents this concept using a symmetric Kullback-Leibler divergence and discuss the features of this method.
Original language | English |
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Title of host publication | 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1126-1129 |
Number of pages | 4 |
ISBN (Electronic) | 9781479959556 |
DOIs | |
Publication status | Published - 2014 Feb 18 |
Externally published | Yes |
Event | 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 - Kitakyushu, Japan Duration: 2014 Dec 3 → 2014 Dec 6 |
Other
Other | 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 |
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Country/Territory | Japan |
City | Kitakyushu |
Period | 14/12/3 → 14/12/6 |
Keywords
- ensemble learning
- exponential mixture model
- parameter estimation
- symmetric Kullback-Leibler divergence
ASJC Scopus subject areas
- Software
- Artificial Intelligence