Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICPR.2004.1334305
Title: Applying the conjugate gradient method for text document categorization
Authors: Tam, V.
Setiono, R. 
Santoso, A.
Keywords: Conjugate Gradient Method
Document Classification
Linear Least Squares Fit
Performance Measures
Issue Date: 2004
Source: Tam, V., Setiono, R., Santoso, A. (2004). Applying the conjugate gradient method for text document categorization. Proceedings - International Conference on Pattern Recognition 2 : 558-561. ScholarBank@NUS Repository. https://doi.org/10.1109/ICPR.2004.1334305
Abstract: In this paper, we investigate the effectiveness of two different methods to solve the linear least squares fit (LLSF) problem for document categorization. The first method is the Singular Value Decomposition (SVD) method that has been previously used to solve the document categorization problem. The second method is the Conjugate Gradient (CG) method that is one of the most effective algorithms for solving a linear equation problem. However, up to our knowledge, the CG method has never been applied to handle the document classification, problem. Therefore, we compare the effectiveness of these two LLSF methods to categorize text documents. In addition, we examine the effect of using different term weighting schemes on their performance for document classification. Lastly, we compare the performance of the LLSF classifiers against the neighborhood-based Dt-kNN classifier, our best variant of the kNN classifier integrated with a dynamic threshold scheme, on the Reuters 21578 dataset. Besides being the first proposal to use the CG method for document classification, our work opens up many exciting directions for future investigation.
Source Title: Proceedings - International Conference on Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/42750
ISBN: 0769521282
ISSN: 10514651
DOI: 10.1109/ICPR.2004.1334305
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

605
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.