Interpreting Extreme Learning Machine as an Approximation to an Infinite Neural Network
Abstract
Extreme Learning Machine (Elm) is a Neural Network Architecture in Which Hidden Layer Weights Are Randomly Chosen and Output Layer Weights Determined Analytically. We Interpret Elm as an Approximation to a Network with Infinite Number of Hidden Units. the Operation of the Infinite Network is Captured by Neural Network Kernel (Nnk). We Compare Elm and Nnk Both as Part of a Kernel Method and in Neural Network Contextsights Gained from This Analysis Lead Us to Strongly Recommend Model Selection Also on the Variance of Elm Hidden Layer Weights, and Not Only on the Number of Hidden Units, as is Usually Done with Elm. We Also Discuss Some Properties of Elm, Which May Have Been Too Strongly Interpreted in Previous Works.
Recommended Citation
E. Parviainen et al., "Interpreting Extreme Learning Machine as an Approximation to an Infinite Neural Network," KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 65 - 73, SciTePress, Dec 2010.
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Extreme learning machine (ELM); Neural network kernel
International Standard Book Number (ISBN)
978-989842528-7
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 SciTePress, All rights reserved.
Publication Date
01 Dec 2010
