A Faster Model Selection Criterion for Op-Elm and Op-Knn: Hannan-Quinn Criterion
Abstract
The Optimally Pruned Extreme Learning Machine (Opelm) and Optimally Pruned K-Nearest Neighbors (Op-Knn) Algorithms Use the a Similar Methodology based on Random Initialization (Op-Elm) or Knn Initialization (Op-Knn) of a Feedforward Neural Network Followed by Ranking of the Neurons; Ranking is Used to Determine the Best Combination to Retain. This is Achieved by Leave-One-Out (Loo) Crossvalidation. in This Article is Proposed to Use the Hannan-Quinn (Hq) Criterion as a Model Selection Criterion, Instead of Loo. It Proved to Be Efficient and as Good as the Loo One for Both Op-Elm and Op-Knn, While Decreasing Computations by Factors of Four to Five for Op-Elm and Up to 24 for Op-Knn.
Recommended Citation
Y. Miche and A. Lendasse, "A Faster Model Selection Criterion for Op-Elm and Op-Knn: Hannan-Quinn Criterion," ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, pp. 177 - 182, European Symposium on Artificial Neural Networks, Dec 2009.
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-293030709-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 European Symposium on Artificial Neural Networks, All rights reserved.
Publication Date
01 Dec 2009
