| J. McNames, "A nearest trajectory strategy for time series prediction," in Proceedings of the International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling. K. U. Leuven Belgium, 1998. |
....even in the testing phase, some types of classifiers become prohibitively slow. A notable example is the nearest neighbor classifier. Most fast implementations of this classifier work well and provide acceleration only when the feature space dimensionality is low (usually less than 15 dimensions) [9]. We are currently working on the application of the method to a large scale OCR problem. Preliminary results show that significant acceleration (one to two orders of magnitude) is observed using the Nene Nayar variant of the nearest neighbor classifier [10] applied to the salient features ....
J. McNames, "A nearest trajectory strategy for time series prediction", Proc. International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling, KU Leuven, Belgium, pp. 112128, 1998.
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James McNames, "A nearest trajectory strategy for time series prediction," in Proceedings of the International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling, Katholieke Universiteit Leuven, Belgium, July 1998, pp. 112--128.
No context found.
J. McNames, "A nearest trajectory strategy for time series prediction," in Proceedings of the International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling. K. U. Leuven Belgium, 1998.
No context found.
McNames J., "A nearest trajectory strategy for time series prediction," Proceedings of the International Workshop on Advanc ed Black-Box Techniques for Nonlinear Modeling, July 8-10, 1998.
No context found.
McNames J., "A nearest trajectory strategy for time series prediction," Proceedings of the International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling, July 8-10, 1998.
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