IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Statistical Image Object Recognition using Mixture Densities
Journal of Mathematical Imaging and Vision
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
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
A Probabilistic View on Tangent Distance
Mustererkennung 2000, 22. DAGM-Symposium
Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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In this paper, we present a combined classification approach called the 'virtual test sample method'. Contrary to classifier combination, where the outputs of a number of classifiers are used to come to a combined decision for a given observation, we use multiple instances generated from the original observation and a single classifier to compute a combined decision. In our experiments, the virtual test sample method is used to improve the performance of a statistical classifier based on Gaussian mixture densities. We show that this approach has some desirable theoretical properties and performs very well, especially when combined with the use of invariant distance measures. In the experiments conducted throughout this work, we obtained an excellent error rate of 2.2% on the original US Postal Service task.