Please use this identifier to cite or link to this item:
https://doi.org/10.1023/A:1023202221875
Title: | On machine learning methods for Chinese document categorization | Authors: | He, J. Tan, A.-H. Tan, C.-L. |
Keywords: | Comparative experiments Machine learning Text categorization |
Issue Date: | 2003 | Citation: | He, J., Tan, A.-H., Tan, C.-L. (2003). On machine learning methods for Chinese document categorization. Applied Intelligence 18 (3) : 311-322. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1023202221875 | Abstract: | This paper reports our comparative evaluation of three machine learning methods, namely k Nearest Neighbor (kNN), Support Vector Machines (S VM), and Adaptive Resonance Associative Map (ARAM) for Chinese document categorization. Based on two Chinese corpora, a series of controlled experiments evaluated their learning capabilities and efficiency in mining text classification knowledge. Benchmark experiments showed that their predictive performance were roughly comparable, especially on clean and well organized data sets. While kNN and ARAM yield better performances than SVM on small and clean data sets, SVM and ARAM significantly outperformed kNN on noisy data. Comparing efficiency, kNN was notably more costly in terms of time and memory than the other two methods. SVM is highly efficient in learning from well organized samples of moderate size, although on relatively large and noisy data the efficiency of SVM and ARAM are comparable. | Source Title: | Applied Intelligence | URI: | http://scholarbank.nus.edu.sg/handle/10635/39283 | ISSN: | 0924669X | DOI: | 10.1023/A:1023202221875 |
Appears in Collections: | Staff Publications |
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.