Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural networks and the bias/variance dilemma
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Nested Partitions Properties for Spatial Content Image Retrieval
International Journal of Digital Library Systems
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This paper applies a hierarchical classifier to two image recognition tasks. At the heart of this classifier, like many other classifiers, is a distance metric for determining the similarity of pairs of images. As the generalisation performance is often strongly related to the effectiveness of this measure, this paper develops a measure that is statistically more reliable than some metrics, but does not discard discriminating information, often regarded as “noise”. In addition, it may be computed quickly. This paper also experimentally shows that the metric may be used in the hierarchical classifier to yield error rates far lower to those based on the Euclidean distance metric on the two image recognition tasks. Furthermore, it gives the lowest reported error rate (2.63%) as well as the best training and classification times for a face recognition task.