Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TLDR
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.Abstract:
The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.read more
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References
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Proceedings ArticleDOI
Comparison of classifier methods: a case study in handwritten digit recognition
Léon Bottou,Corinna Cortes,Corinna Cortes,John S. Denker,John S. Denker,Harris Drucker,Harris Drucker,Isabelle Guyon,Lawrence D. Jackel,Yann LeCun,U.A. Muller,E. Sackinger,Patrice Y. Simard,Patrice Y. Simard,Vladimir Vapnik +14 more
TL;DR: This paper compares the performance of several classifier algorithms on a standard database of handwritten digits by considering not only raw accuracy, but also training time, recognition time, and memory requirements.
Journal ArticleDOI
Classification into two Multivariate Normal Distributions with Different Covariance Matrices
T. W. Anderson,R. R. Bahadur +1 more
TL;DR: In this paper, linear procedures for classifying an observation as coming from one of two multivariate normal distributions are studied in the case that the two distributions differ both in mean vectors and covariance matrices.
Neural-Network and k-Nearest-neighbor Classifiers
Jane Bromley,E. Sackinger +1 more
TL;DR: The performance of a state-of-the-art neural network classifier for hand-written digits is compared to that of a k-nearest-neighbor method and to human performance.