We address the problem of multi-labelled text classification using word-sequence kernels. However, rather than applying them with Support Vector Machine as in previous work, we chose a classifier based on Gaussian Processes. This is a probabilistic non-parametric method that retains a sound probabilistic semantics while overcoming the limitations of parametric methods. We present the empirical evaluation of our approach on the standard Reuters-21578 datasets.
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