Combining Support Vector Machines and Information Gain Ranking for Classification of Mars McMurdo Panorama Images

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dc.contributor.author Barnes, David Preston
dc.contributor.author Shang, Changjing
dc.date.accessioned 2010-10-04T08:14:27Z
dc.date.available 2010-10-04T08:14:27Z
dc.date.issued 2010
dc.identifier.citation Barnes , D P & Shang , C 2010 , ' Combining Support Vector Machines and Information Gain Ranking for Classification of Mars McMurdo Panorama Images ' pp. 1061-1064 . en
dc.identifier.other PURE: 151621
dc.identifier.other dspace: 2160/5736
dc.identifier.uri http://hdl.handle.net/2160/5736
dc.description C. Shang and D. Barnes. Combining Support Vector Machines and Information Gain Ranking for Classification of Mars McMurdo Panorama Images. Proceedings of the 17th IEEE International Conference on Image Processing, pp. 1061-1064. Sponsorship: Royal Academy of Engineering and Daphne Jackson Trust. en
dc.description.abstract This paper presents a novel application of support vector machine (SVM) based classifiers for Mars terrain image classification. SVMs are applied in conjunction with information gain ranking (IGR) that allows the induction of informative feature subsets from sample descriptions of feature vectors of a higher dimensionality. Such an integrated use of IGR and SVMs helps to enhance the effectiveness and efficiency of the classifiers, minimizing redundant and noisy features. This work is supported with comparative studies – the resultant SVM-based classifiers generally outperform MLP and KNN-based classifiers and those which use PCA-returned features. en
dc.format.extent 4 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Combining Support Vector Machines and Information Gain Ranking for Classification of Mars McMurdo Panorama Images en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Intelligent Robotics Group en
dc.description.status Non peer reviewed en


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