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Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization
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SVM Classification Using Sequences of Phonemes and Syllables
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IEEE Transactions on Knowledge and Data Engineering
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Selecting text features for gene name classification: from documents to terms
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
A probabilistic model for text kernels
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Very sparse random projections
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Feature selection methods for text classification
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Pattern Recognition Letters
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Pattern Recognition Letters
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Expert Systems with Applications: An International Journal
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Pattern Recognition Letters
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Projected-prototype based classifier for text categorization
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The choice of the kernel function is crucial to most applications of support vector machines. In this paper, however, we show that in the case of text classification, term-frequency transformations have a larger impact on the performance of SVM than the kernel itself. We discuss the role of importance-weights (e.g. document frequency and redundancy), which is not yet fully understood in the light of model complexity and calculation cost, and we show that time consuming lemmatization or stemming can be avoided even when classifying a highly inflectional language like German.