Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/85902
Title: Comparison of extreme learning machine with support vector machine for text classification
Authors: Liu, Y. 
Loh, H.T. 
Tor, S.B.
Issue Date: 2005
Citation: Liu, Y.,Loh, H.T.,Tor, S.B. (2005). Comparison of extreme learning machine with support vector machine for text classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3533 LNAI : 390-399. ScholarBank@NUS Repository.
Abstract: Extreme Learning Machine, ELM, is a recently available learning algorithm for single layer feedforward neural network. Compared with classical learning algorithms in neural network, e.g. Back Propagation, ELM can achieve better performance with much shorter learning time. In the existing literature, its better performance and comparison with Support Vector Machine, SVM, over regression and general classification problems catch the attention of many researchers. In this paper, the comparison between ELM and SVM over a particular area of classification, i.e. text classification, is conducted. The results of benchmarking experiments with SVM show that for many categories SVM still outperforms ELM. It also suggests that other than accuracy, the indicator combining precision and recall, i.e. F1 value, is a better performance indicator. © Springer-Verlag Berlin Heidelberg 2005.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/85902
ISBN: 3540265511
ISSN: 03029743
Appears in Collections:Staff Publications

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