Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models
Abstract
This Paper Presents an Extension of the Well-Known Extreme Learning Machines (Elms). the Main Goal is to Provide Probabilities as Outputs for Multiclass Classification Problems. Such Information is More Useful in Practice Than Traditional Crisp Classification Outputs. in Summary, Gaussian Mixture Models Are Used as Post-Processing of Elms. in that Context, the Proposed Global Methodology is Keeping the Advantages of Elms (Low Computational Time and State of the Art Performances) and the Ability of Gaussian Mixture Models to Deal with Probabilities. the Methodology is Tested on 3 Toy Examples and 3 Real Datasets. as a Result, the Global Performances of Elms Are Slightly Improved and the Probability Outputs Are Seen to Be Accurate and Useful in Practice.
Recommended Citation
E. Eirola et al., "Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9095, pp. 153 - 164, Springer, Jan 2015.
The definitive version is available at https://doi.org/10.1007/978-3-319-19222-2_13
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Classification; Extreme learning machines; Gaussian mixture models; Internet security; Leave-one-out cross-validation; Machine learning; Multiclass classification; Neural network; Parental control; PRESS statistics
International Standard Book Number (ISBN)
978-331919221-5
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2015
