Stream-Based Extreme Learning Machine Approach for Big Data Problems
Joint Authors
Horta, Euler Guimarães
Castro, Cristiano Leite de
Braga, Antônio Pádua
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-09-14
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Big Data problems demand data models with abilities to handle time-varying, massive, and high dimensionaldata.
In this context, Active Learning emerges as an attractive technique for the development of highperformance models using few data.
The importance of Active Learning for Big Data becomes more evidentwhen labeling cost is high and data is presented to the learner via data streams.
This paper presents a novelActive Learning method based on Extreme Learning Machines (ELMs) and Hebbian Learning.
Linearizationof input data by a large size ELM hidden layer turns our method little sensitive to parameter setting.
Overfitting is inherently controlled via the Hebbian Learning crosstalk term.
We also demonstrate that asimple convergence test can be used as an effective labeling criterion since it points out to the amount oflabels necessary for learning.
The proposed method has inherent properties that make it highly attractiveto handle Big Data: incremental learning via data streams, elimination of redundant patterns, and learningfrom a reduced informative training set.
Experimental results have shown that our method is competitivewith some large-margin Active Learning strategies and also with a linear SVM.
American Psychological Association (APA)
Horta, Euler Guimarães& Castro, Cristiano Leite de& Braga, Antônio Pádua. 2015. Stream-Based Extreme Learning Machine Approach for Big Data Problems. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-17.
https://search.emarefa.net/detail/BIM-1072945
Modern Language Association (MLA)
Horta, Euler Guimarães…[et al.]. Stream-Based Extreme Learning Machine Approach for Big Data Problems. Mathematical Problems in Engineering No. 2015 (2015), pp.1-17.
https://search.emarefa.net/detail/BIM-1072945
American Medical Association (AMA)
Horta, Euler Guimarães& Castro, Cristiano Leite de& Braga, Antônio Pádua. Stream-Based Extreme Learning Machine Approach for Big Data Problems. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-17.
https://search.emarefa.net/detail/BIM-1072945
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1072945