Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Fast adaptive LDA using quasi-Newton algorithm
Pattern Recognition Letters
Algorithms and networks for accelerated convergence of adaptive LDA
Pattern Recognition
Applications of a Kushner and Clark lemma to general classes of stochastic algorithms
IEEE Transactions on Information Theory
On self-organizing algorithms and networks for class-separability features
IEEE Transactions on Neural Networks
On the convergence of the mean shift algorithm in the one-dimensional space
Pattern Recognition Letters
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In this paper, new adaptive learning algorithms and correspondent networks are presented in order to extract optimal features from a sequence of multidimensional Gaussian data. For this purpose, novel adaptive algorithms for the estimation of the square root of the inverse covariance matrix @S^-^1^/^2 are introduced and applied for Gaussian optimal feature extraction. New adaptive algorithms are drawn by optimization of an appropriate cost function that is presented for the first time. Based on the proposed adaptive algorithms, related networks are implemented in order to extract optimal features from a sequence of multidimensional Gaussian data. Adaptive nature of the new feature extraction methods makes them appropriate for on-line pattern recognition applications. Experimental results using multidimensional Gaussian data demonstrated the effectiveness of the new adaptive feature extraction methods.