Multiple Multivariate Regression and Global Optimization in a Large Scale Thermodynamical Application

Hugo Zaragoza 1 Patrick Gallinari 1
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : We describe a large scale real-world application of neural networks for the modelization of heat radiation emitted by a source and observed through the atmosphere. For this problem, thousands of regressors need to be trained and incorporated into a single model of the process. On such large scale applications, standard techniques for the control of complexity are impossible to implement. We investigate the interest of i) integrating several regressors into a single neural network, and ii) refining the learned functions by optimizing simultaneously all regressors over a global function. The two approaches described offer a solution to these problems, and were crucial for the development of a fast and accurate model of radiation intensity.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01623874
Contributor : Lip6 Publications <>
Submitted on : Wednesday, October 25, 2017 - 5:31:20 PM
Last modification on : Thursday, March 21, 2019 - 1:13:40 PM

Links full text

Identifiers

Citation

Hugo Zaragoza, Patrick Gallinari. Multiple Multivariate Regression and Global Optimization in a Large Scale Thermodynamical Application. 7th International Conference of Artificial Neural Networks, Oct 1997, Lausanne, Switzerland. pp.861-866, ⟨10.1007/BFb0020262⟩. ⟨hal-01623874⟩

Share

Metrics

Record views

35