Machine Learning
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Approximate Nearest Neighbor Algorithms for Hausdorff Metrics via Embeddings
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Reference Set Thinning for the k-Nearest Neighbor Decision Rule
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Fast minimization of structural risk by nearest neighbor rule
IEEE Transactions on Neural Networks
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We present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic classification problem. We propose a fast reference set thinning algorithm on the training data set similar to a support vector machine approach. We then show that the nearest neighbor rule based on the reduced set implements the structural risk minimization principle, in a manner, which does not involve selection of a convenient feature space. Simulation results on real data indicate that this method significantly reduces the computational cost of the conventional support vector machines, and achieves a nearly comparable test error performance.