Text classification with a Primal SVM endowed with domain knowledge
Emilio Parrado-Hernandez and David Hardoon
Unpublished, London, UK.
In this paper we solve a document classification task by incorporating
prior/domain knowledge onto the SVM. The algorithm consists in to
learn a prior classifier in the primal space (words) from an `external' source
of information to the text classification itself: patterns of reader's
eyes movements when reading relevant words for discriminating
texts. This prior weight vector is then plugged into the SVM optimisation
in the primal space. Experimental results include a comparison of the proposed algorithm
with plain SVM classifiers and with an alternative way of mixing
textual and eye information based on the SVM-2K.