The nature of statistical learning theory
The nature of statistical learning theory
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The kernelHMM: learning kernel combinations in structured output domains
Proceedings of the 29th DAGM conference on Pattern recognition
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This paper explores several kernels in the context of text classification. A novel view of how documents might have been created is introduced and kernels are derived from this framework. The relations between these kernels as well as to the Gaussian kernel are discussed. Moreover, the popular tf-idf weighting scheme will be derived as a natural consequence. Finally, the kernels have been evaluated on the Reuters Corpus Volume I newswire database to assess their quality in a topic classification application.