<XML><RECORDS><RECORD><REFERENCE_TYPE>0</REFERENCE_TYPE><REFNUM>9169</REFNUM><AUTHORS><AUTHOR>Polajnar,T.</AUTHOR><AUTHOR>Rogers,S.</AUTHOR><AUTHOR>Girolami,M.</AUTHOR></AUTHORS><YEAR>2010</YEAR><TITLE>Protein Interaction Detection in Sentences via Gaussian Processes: A preliminary evaluation</TITLE><PLACE_PUBLISHED>International Journal of Data Mining and Bioinformatics</PLACE_PUBLISHED><PUBLISHER>N/A</PUBLISHER><LABEL>Polajnar:2010:9169</LABEL><KEYWORDS><KEYWORD>text; classification; gaussian process; kernel; text mining; support vector machine</KEYWORD></KEYWORDS<ABSTRACT>Classification methods are vital for efficient access of knowledge hidden in biomedical publications. Support vector machines (SVMs) are modern non-parametric deterministic classifiers that produce state of the art performances in text mining, and across other disciplines, while reducing the need for feature engineering. In this paper we offer 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. To this end, 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.</ABSTRACT></RECORD></RECORDS></XML>