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13 October 2000 Classification of car in lane using support vector machines
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Support Vector Machines (SVMs) have become popular due to their accuracy in classifying sparse data sets. Their computational time can be virtually independent of the size of the feature vector. SVMs have been shown to out perform other learning machines on many data sets. In this paper, we use SVMs to detect a car in a lane of traffic. Digital pictures of various driving situations are used. The results from the SVM algorithm are compared to results from a standard neural network approach.
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Michael Del Rose, David J. Gorsich, and Robert E. Karlsen "Classification of car in lane using support vector machines", Proc. SPIE 4120, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III, (13 October 2000);

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