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 training samples 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 generalize this method.
| Original language | English |
|---|---|
| Pages (from-to) | 2349-2353 |
| Number of pages | 5 |
| Journal | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
| Volume | E98A |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 2015 Nov 1 |
| Externally published | Yes |
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Keywords
- Ensemble learning
- Exponential mixture model
- Parameter estimation
- Symmetric Kullback-Leibler divergence
ASJC Scopus subject areas
- Signal Processing
- Computer Graphics and Computer-Aided Design
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
Unsupervised weight parameter estimation for exponential mixture distribution based on symmetric kullback-leibler divergence. / Uchida, Masato.
In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E98A, No. 11, 01.11.2015, p. 2349-2353.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Unsupervised weight parameter estimation for exponential mixture distribution based on symmetric kullback-leibler divergence
AU - Uchida, Masato
PY - 2015/11/1
Y1 - 2015/11/1
N2 - 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 training samples 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 generalize this method.
AB - 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 training samples 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 generalize this method.
KW - Ensemble learning
KW - Exponential mixture model
KW - Parameter estimation
KW - Symmetric Kullback-Leibler divergence
UR - http://www.scopus.com/inward/record.url?scp=84947998264&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84947998264&partnerID=8YFLogxK
U2 - 10.1587/transfun.E98.A.2349
DO - 10.1587/transfun.E98.A.2349
M3 - Article
VL - E98A
SP - 2349
EP - 2353
JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
SN - 0916-8508
IS - 11
ER -