DocumentCode :
1559319
Title :
A neural network approach to statistical pattern classification by `semiparametric´ estimation of probability density functions
Author :
Tråvén, Hans G C
Author_Institution :
Dept. of Numerical Anal. & Comput. Sci., R. Inst. of Technol., Stockholm, Sweden
Volume :
2
Issue :
3
fYear :
1991
fDate :
5/1/1991 12:00:00 AM
Firstpage :
366
Lastpage :
377
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 :
neural nets; parameter estimation; pattern recognition; self-adjusting systems; statistical analysis; cross-class data pooling; invariant projections; multispectral image classification; near-optimal nonlinear classifiers; neural network; probability density functions; self-organizing technique; semiparametric estimation; speech recognition; statistical pattern classification; subspace partitioning; sum-of-product units; Clustering algorithms; Density functional theory; Design methodology; Design optimization; Distributed computing; Neural networks; Organizing; Pattern classification; Probability density function; Stochastic processes;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.97913
Filename :
97913
Link To Document :
بازگشت