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Neural Computing and Applications

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01.03.2013 | Extreme Learning Machine’s Theory & Application

Text categorization based on regularization extreme learning machine

verfasst von: Wenbin Zheng, Yuntao Qian, Huijuan Lu

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2013

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Abstract

This article proposes a novel approach for text categorization based on a regularization extreme learning machine (RELM) in which its weights can be obtained analytically, and a bias-variance trade-off could be achieved by adding a regularization term into the linear system of single-hidden layer feedforward neural networks. To fit the input scale of RELM, the latent semantic analysis was used to represent text for dimensionality reduction. Moreover, a classification algorithm based on RELM was developed including the uni-label (i.e., a document can only be assigned to a unique category) and multi-label (i.e., a document can be assigned to multiple categories simultaneously) situations. The experimental results in two benchmarks show that the proposed method can produce good performance in most cases, and it could learn faster than popular methods such as feedforward neural networks or support vector machine.

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Titel: Text categorization based on regularization extreme learning machine
verfasst von: Wenbin Zheng
Yuntao Qian
Huijuan Lu
Publikationsdatum: 01.03.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3-4/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0808-y

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