NDLI: Comparison Of Entropy And Mean Square Error Criteria In Adaptive System Training Using Higher Order Statistics (2000)

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Comparison Of Entropy And Mean Square Error Criteria In Adaptive System Training Using Higher Order Statistics (2000)

Content Provider	CiteSeerX
Author	Erdogmus, Deniz Principe, Jose C.
Description	The error-entropy-minimization approach in adaptive system training is investigated. The effect of Parzen windowing on the location of the global minimum of entropy has been investigated. An analytical proof that shows the global minimum of the entropy is a local minimum, possibly the global minimum, of the nonparametrically estimated entropy using Parzen windowing with Gaussian kernels. The performances of error-entropy-minimization and the mean-square-errorminimization criteria are compared in short-term prediction of a chaotic time series. Statistical behavior of the estimation errors and the higher order central moments of the time series data and its predictions are utilized as the comparison criteria. 1. INTRODUCTION Starting with the early work of Wiener [1] on adaptive filters, mean square error (MSE) has been almost exclusively employed in the training of all adaptive systems including artificial neural networks. There were mainly two reasons lying behind this choice: Analyti...
File Format	PDF
Language	English
Publisher Date	2000-01-01
Publisher Institution	Proceedings of the Second International Workshop on Independent Component Analysis and Blind Signal Separation
Access Restriction	Open
Subject Keyword	Short-term Prediction Analytical Proof Gaussian Kernel Mean Square Error Criterion Early Work Chaotic Time Series Statistical Behavior Mean-square-errorminimization Criterion Adaptive Filter Comparison Criterion Global Minimum Error-entropy-minimization Approach Artificial Neural Network Order Statistic Mean Square Error Adaptive System Order Central Moment Time Series Data Local Minimum Estimation Error Adaptive System Training
Content Type	Text
Resource Type	Article

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