Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Distributional word clusters vs. words for text categorization
The Journal of Machine Learning Research
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A New Text Categorization Technique Using Distributional Clustering and Learning Logic
IEEE Transactions on Knowledge and Data Engineering
Experiments on summary-based opinion classification
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
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Text Categorization (TC) remains as a potential application area for linear support vector machines (SVMs). Among the numerous linear SVM formulations, we bring forward linear PSVM together with recently proposed distributional clustering (DC) of words to realize its potential in TC realm. DC has been presented as an efficient alternative to conventionally used feature selection in TC. It has been shown that, DC together with linear SVM drastically brings down the dimensionality of text documents without any compromise in classification performance. In this paper we use linear PSVM and its extension Fuzzy PSVM (FPSVM) together with DC for TC. We present experimental results comparing PSVM/FPSVM with linear SVM light and SVMlin on popular WebKB text corpus. Through numerous experiments on subsets of WebKB, we reveal the merits of PSVM and FPSVM over other linear SVMs.