Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Evaluating text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
Relevance feedback and other query modification techniques
Information retrieval
Optimization of relevance feedback weights
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
RCV1: A New Benchmark Collection for Text Categorization Research
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
Exploiting concept clumping for efficient incremental news article categorization
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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A fast and accurate linear supervised algorithm is presented which compares favorably to other state of the art algorithms over several real data collections on the problem of text categorization. Although it has been already presented in [6], no proof of its convergence is given. From the geometric intuition of the algorithm it is evident that it is not a Perceptron or a gradient descent algorithm thus an algebraic proof of its convergence is provided in the case of linearly separable classes. Additionally we present experimental results on many standard text classification datasets and artificially generated linearly separable datasets. The proposed algorithm is very simple to use and easy to implement and it can be used in any domain without any modification on the data or parameter estimation.