Prediction of Bank Telemarketing with Co-training of Mixture-of-Experts and MLP

2016 ◽  
pp. 52-59
Author(s):  
Jae-Min Yu ◽  
Sung-Bae Cho
Keyword(s):  
Mixture Of Experts

LRMoE: An R Package for Flexible Actuarial Loss Modelling Using Mixture of Experts Regression Model

2020 ◽  
Author(s):  
Spark C. Tseung ◽  
Andrei Badescu ◽  
Tsz Chai Fung ◽  
Xiaodong Sheldon Lin
Keyword(s):  
Regression Model ◽  
R Package ◽  
Mixture Of Experts ◽  
Loss Modelling

scholarly journals Why and how the brain weights contributions from a mixture of experts

2021 ◽  
Vol 123 ◽  
pp. 14-23
Author(s):  
John P. O’Doherty ◽  
Sang Wan Lee ◽  
Reza Tadayonnejad ◽  
Jeff Cockburn ◽  
Kyo Iigaya ◽  
...  
Keyword(s):  
Mixture Of Experts ◽  
The Brain

scholarly journals Channel Gain Cartography via Mixture of Experts

2020 ◽  
Author(s):  
Luis M. Lopez-Ramos ◽  
Yves Teganya ◽  
Baltasar Beferull-Lozano ◽  
Seung-Jun Kim
Keyword(s):  
Channel Gain ◽  
Mixture Of Experts

mvClaim : an R package for multivariate general insurance claims severity modelling

2021 ◽  
pp. 1-17
Author(s):  
Sen Hu ◽  
T. Brendan Murphy ◽  
Adrian O’Hagan
Keyword(s):  
R Package ◽  
Finite Mixture ◽  
Current Version ◽  
Mixture Of Experts ◽  
Insurance Claim ◽  
General Insurance ◽  
Gamma Distributions ◽  
Approach Theory ◽  
Modelling Approach ◽  
Model Family

Abstract The mvClaim package in R provides flexible modelling frameworks for multivariate insurance claim severity modelling. The current version of the package implements a parsimonious mixture of experts (MoE) model family with bivariate gamma distributions, as introduced in Hu et al., and a finite mixture of copula regressions within the MoE framework as in Hu & O’Hagan. This paper presents the modelling approach theory briefly and the usage of the models in the package in detail. This package is hosted on GitHub at https://github.com/senhu/.

Teacher-directed learning in view-independent face recognition with mixture of experts using single-view eigenspaces

2008 ◽  
Vol 345 (2) ◽  
pp. 87-101 ◽  
Author(s):  
Reza Ebrahimpour ◽  
Ehsanollah Kabir ◽  
Mohammad Reza Yousefi
Keyword(s):  
Face Recognition ◽  
Mixture Of Experts ◽  
Single View ◽  
Directed Learning

scholarly journals Boosted Mixture of Experts: An Ensemble Learning Scheme

1999 ◽  
Vol 11 (2) ◽  
pp. 483-497 ◽  
Author(s):  
Ran Avnimelech ◽  
Nathan Intrator
Keyword(s):  
Recognition Task ◽  
Mixture Of Experts ◽  
Data Set ◽  
Combination Model ◽  
Digit Recognition ◽  
Learning Procedure ◽  
Nist Database ◽  
Boosting Algorithm ◽  
Regression Problems ◽  
Classification And Regression

We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hinton, 1991), applied to classification, or a variant of the boosting algorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner. If viewed as a variant of the boosting algorithm, its main gain is the use of a dynamic combination model for the outputs of the networks. Results are demonstrated on a synthetic example and a digit recognition task from the NIST database and compared with classical ensemble approaches.

Sign in / Sign up

Export Citation Format

Share Document