Image Classification Using SVMs: One-against-One Vs One-against-All

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dc.contributor.authorGidudu, Anthony,
dc.contributor.authorHulley, Gregg
dc.contributor.authorMarwala, Tshilidzi
dc.date.accessioned2023-02-01T20:21:45Z
dc.date.available2023-02-01T20:21:45Z
dc.date.issued2007
dc.description.abstractSupport Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate and are effective even when using a small training sample. By their nature SVMs are essentially binary classifiers, however, they can be adopted to handle the multiple classification tasks common in remote sensing studies. The two approaches commonly used are the One-Against-One (1A1) and One-Against-All (1AA) techniques. In this paper, these approaches are evaluated in as far as their impact and implication for land cover mapping. The main finding from this research is that whereas the 1AA technique is more predisposed to yielding unclassified and mixed pixels, the resulting classification accuracy is not significantly different from 1A1 approach. It is the authors conclusion therefore that ultimately the choice of technique adopted boils down to personal preference and the uniqueness of the dataset at hand.en_US
dc.identifier.citationAnthony, G., Gregg, H., & Tshilidzi, M. (2007). Image classification using SVMs: one-against-one vs one-against-all. arXiv preprint arXiv:0711.2914.en_US
dc.identifier.urihttps://arxiv.org/abs/0711.2914
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7461
dc.language.isoenen_US
dc.publisherarXiv preprint arXiven_US
dc.subjectSupport Vector Machinesen_US
dc.subjectOne-against-oneen_US
dc.subjectOne-against-Allen_US
dc.titleImage Classification Using SVMs: One-against-One Vs One-against-Allen_US
dc.typeOtheren_US
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