Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Comparison of extreme learning machine with support vector machine for text classification

Liu, Ying, Loh, Han Tong and Tor, Shu Beng 2005. Comparison of extreme learning machine with support vector machine for text classification. Presented at: 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE 2005), Bari, Italy, 22-24 June 2005. Innovations in Applied Artificial Intelligence: 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2005, Bari, Italy, June 22-24, 2005. Proceedings. Lecture Notes in Computer Science , vol. 3533. Berlin: Springer, pp. 390-399. 10.1007/11504894_55

Full text not available from this 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. F 1 value, is a better performance indicator.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: Springer
ISBN: 9783540265511
ISSN: 0302-9743
Last Modified: 04 Jun 2017 05:26
URI: http://orca.cf.ac.uk/id/eprint/51334

Citation Data

Cited 18 times in Google Scholar. View in Google Scholar

Actions (repository staff only)

Edit Item Edit Item