Polajnar, T; Rogers, S; Girolami, M; (2011) Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation. INT J DATA MIN BIOIN , 5 (1) 52 - 72.
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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.
|Title:||Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation|
|Keywords:||TM, text mining, GP, Gaussian process, SVM, support vector machine, protein interaction, sentence classification, SUPPORT VECTOR MACHINES, MULTINOMIAL PROBIT REGRESSION, CLASSIFICATION, TEXT, SVM|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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