Abstract
Two or three hundred different pattern classification algorithms have been suggested in literature during the last 50 years. The main objective of this chapter is to review a selection of known statistical algorithms that can be obtained or improved by training ANN-based classification systems. The first selection contains seven statistical algorithms that can be obtained while training linear and non-linear single layer perceptrons and the second selection contains algorithms that can be approached in ANN training after deriving new non-linear features from the original ones. Particular attention is given to methods which can be used to structure the covariance matrices and describe them by a small number of parameters. This approach is not very popular in statistical pattern recognition, however, together with utilisation of neural networks, it becomes a powerful tool to solve problems in small training-set situations.
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© 2001 Springer-Verlag London Limited
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Raudys, Š. (2001). Taxonomy of Pattern Classification Algorithms. In: Statistical and Neural Classifiers. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-0359-2_2
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DOI: https://doi.org/10.1007/978-1-4471-0359-2_2
Publisher Name: Springer, London
Print ISBN: 978-1-85233-297-6
Online ISBN: 978-1-4471-0359-2
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