document: Gaining insights into support vector machine pattern classifiers using projection-based tour methods This paper discusses visual methods that can be used to understand and interpret the results of classification using support vector machines ( SVM ) on data with continuous real-valued variables . SVM induction algorithms build pattern classifiers by identifying a maximal margin separating hyperplane from training examples in high dimensional pattern spaces or spaces induced by suitable nonlinear kernel transformations over pattern spaces . SVM have been demonstrated to be quite effective in a number of practical pattern classification tasks . Since the separating hyperplane is defined in terms of more than two variables it is necessary to use visual techniques that can navigate the viewer through high-dimensional spaces . We demonstrate the use of projection-based tour methods to gain useful insights into SVM classifiers with linear kernels on 8-dimensional data .
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