A neural network approach to statistical pattern classification by 'semiparametric' estimation of probability density functions
- 1 May 1991
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 2 (3) , 366-377
- https://doi.org/10.1109/72.97913
Abstract
A method for designing near-optimal nonlinear classifiers, based on a self-organizing technique for estimating probability density functions when only weak assumptions are made about the densities, is described. The method avoids disadvantages of other existing methods by parametrizing a set of component densities from which the actual densities are constructed. The parameters of the component densities are optimized by a self-organizing algorithm, reducing to a minimum the labeling of design samples. All the required computations are realized with the simple sum-of-product units commonly used in connectionist models. The density approximations produced by the method are illustrated in two dimensions for a multispectral image classification task. The practical use of the method is illustrated by a small speech recognition problem. Related issues of invariant projections, cross-class pooling of data, and subspace partitioning are discussed.Keywords
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