Paper Info
Title
Unsupervised weight parameter estimation for exponential mixture distribution based on symmetric Kullback-Leibler divergence
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.
Year
DOI
Venue
2014
10.1109/SCIS-ISIS.2014.7044722
IEICE Transactions
Keywords
DocType
Volume
exponential distribution,inference mechanisms,parameter estimation,advanced reasoning,component probability distributions,exponential mixture,exponential mixture distribution,performance evaluation,probability distribution,stochastic model,symmetric kullback-leibler divergence,unsupervised weight parameter estimation,ensemble learning,exponential mixture model
Conference
98-A
Issue
ISSN
Citations 
11
2377-6870
0
PageRank 
References 
Authors
0.34
5
1
Name
Order
Citations
PageRank
Masato Uchida115020.79