title: Protein interaction detection in sentences via Gaussian processes: a preliminary evaluation.
abstract: The non-parametric deterministic Support Vector Machines (SVMs) produce high levels of performances in text classification. This article offers a much needed evaluation of the Gaussian Process (GP) classifier, as a non-parametric probabilistic analogue to SVMs, which has been rarely applied to text classification. We provide an extensive experimental comparison of the performance and properties of these competing classifiers on the challenging problem of protein interaction detection in biomedical publications. Our results show that GPs can match the performance of SVMs without the need for costly margin parameter tuning, whilst offering the advantage of an extendable probabilistic framework for text classification.
authors.affiliation: Department of Computing Science, University of Glasgow, Glasgow, G12 8QQ, Scotland. tamara@dcs.gla.ac.uk
authors.forename: Tamara
authors.initials: T
authors.lastname: Polajnar
authors.forename: Simon
authors.initials: S
authors.lastname: Rogers
authors.forename: Mark
authors.initials: M
authors.lastname: Girolami
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry / Q000378:metabolism
chemical_list: D011506:Proteins