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dc.contributor.author Shang, Changjing
dc.contributor.author Barnes, David Preston
dc.date.accessioned 2011-10-25T15:46:05Z
dc.date.available 2011-10-25T15:46:05Z
dc.date.issued 2011-10-25
dc.identifier.citation Shang , C & Barnes , D P 2011 , ' Classification of Mars McMurdo panorama images using machine learning techniques ' . en
dc.identifier.other PURE: 171801
dc.identifier.other dspace: 2160/7628
dc.identifier.uri http://hdl.handle.net/2160/7628
dc.description C. Shang and D. Barnes. Classification of Mars McMurdo panorama images using machine learning techniques. Proceedings of IJCAI Workshop on AI in Space: Intelligence beyond Planet Earth. 2011. en
dc.description.abstract This paper presents a novel application of advanced machine learning techniques for Mars terrain image classification. Fuzzy-rough feature selection (FRFS) is employed in conjunction with Support Vector Machines (SVMs) to construct image classifiers. These techniques are for the first time, integrated to address problems in space engineering where the images are of many classes and large-scale. The use of FRFS allows the induction of low-dimensionality feature sets from feature patterns of a much higher dimensionality. Experimental results demonstrate that FRFS helps to enhance the efficacy of the conventional classifiers. The resultant SVM-based classifiers which utilise FRFS-selected features generally outperform K-Nearest Neighbours and Decision Tree based clas- sifiers and those which use PCA-returned features. en
dc.language.iso eng
dc.title Classification of Mars McMurdo panorama images using machine learning techniques 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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