A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Two algorithms for nearest-neighbor search in high dimensions
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
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Predicting the class of an observation from its nearest neighbors is one of the earliest approaches in pattern recognition. In addition to their simplicity, nearest neighbor rules have appealing theoretical properties, e.g. the asymptotic error probability of the plain 1-nearest-neighbor (NN) rule is at most twice the Bayes bound, which means zero asymptotic risk in the separable case. But given only a finite number of training examples, NN classifiers are often outperformed in practice. A possible modification of the NN rule to handle separable problems better is the nearest local hyperplane (NLH) approach. In this paper we introduce a new way of NLH classification that has two advantages over the original NLH algorithm. First, our method preserves the zero asymptotic risk property of NN classifiers in the separable case. Second, it usually provides better finite sample performance.