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      A kernel functions analysis for support vector machines for land cover classification

        ,

      International Journal of Applied Earth Observation and Geoinformation

      Elsevier BV

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          A comparison of methods for multiclass support vector machines.

          Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
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            Classification of hyperspectral remote sensing images with support vector machines

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              A survey of image classification methods and techniques for improving classification performance

               D Lu,  Q. Weng (2007)
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                Author and article information

                Journal
                International Journal of Applied Earth Observation and Geoinformation
                International Journal of Applied Earth Observation and Geoinformation
                Elsevier BV
                03032434
                October 2009
                October 2009
                : 11
                : 5
                : 352-359
                Article
                10.1016/j.jag.2009.06.002
                © 2009

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