The Probabilistic Model and Forecasting of Power Load Based on Variational Bayesian Expectation Maximization and Relevance Vector Machine

2018 ◽  
Vol 102 (4) ◽  
pp. 3041-3053 ◽  
Author(s):  
Wegen Gao ◽  
Qigong Chen ◽  
Yuan Ge ◽  
YiQing Huang
Keyword(s):  
Expectation Maximization ◽  
Probabilistic Model ◽  
Relevance Vector Machine ◽  
Variational Bayesian ◽  
Power Load

The probabilistic model and forecasting of power load based on JMAP-ML and Gaussian process

2018 ◽  
Vol 22 (S4) ◽  
pp. 8589-8596
Author(s):  
Wengen Gao ◽  
Qigong Chen ◽  
Yuan Ge ◽  
YiQing Huang
Keyword(s):  
Gaussian Process ◽  
Probabilistic Model ◽  
Power Load

scholarly journals Adaptive Classification on Brain-Computer Interfaces Using Reinforcement Signals

2012 ◽  
Vol 24 (11) ◽  
pp. 2900-2923 ◽  
Author(s):  
A. Llera ◽  
V. Gómez ◽  
H. J. Kappen
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Probabilistic Model ◽  
State Of The Art ◽  
Brain Computer Interfaces ◽  
Adaptive Classification ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Parameter Values

We introduce a probabilistic model that combines a classifier with an extra reinforcement signal (RS) encoding the probability of an erroneous feedback being delivered by the classifier. This representation computes the class probabilities given the task related features and the reinforcement signal. Using expectation maximization (EM) to estimate the parameter values under such a model shows that some existing adaptive classifiers are particular cases of such an EM algorithm. Further, we present a new algorithm for adaptive classification, which we call constrained means adaptive classifier, and show using EEG data and simulated RS that this classifier is able to significantly outperform state-of-the-art adaptive classifiers.

Sign in / Sign up

Export Citation Format

Share Document