PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Text classification with a Primal SVM endowed with domain knowledge
Emilio Parrado-Hernandez and David Hardoon
(2008) Technical Report. Unpublished, London, UK.

Abstract

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Multimodal Integration
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:4968
Deposited By:Emilio Parrado-Hernandez
Deposited On:24 March 2009