Journal ArticleDOI
Average classification accuracy over collections of gaussian problems—common covariance matrix case
A.G. Wacker,T. S. El-Sheikh +1 more
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TLDR
The mean probability of correct classification ( P cr) is calculated over a collection of equiprobable two-class Gaussian problems with a common covariance matrix for each problem with Bayes minimum error classification rule considered.About:
This article is published in Pattern Recognition.The article was published on 1984-01-01. It has received 8 citations till now. The article focuses on the topics: Estimation of covariance matrices & Covariance.read more
Citations
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Journal ArticleDOI
The elements of real analysis (2nd edition), by Robert G. Bartle. Pp xv, 480. £10. 1976. SBM 0 471 05464 X (Wiley)
TL;DR: A Glimpse at Set Theory: The Topology of Cartesian Spaces and the Functions of One Variable.
Journal ArticleDOI
Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
TL;DR: Two types of modified quadratic disriminant functions (MQDF1, MQDF2) which are less sensitive to the estimation error of the covariance matrices are proposed.
Journal ArticleDOI
Comparative analysis of statistical pattern recognition methods in high dimensional settings
TL;DR: The simulations identified regularized discriminant analysis as the overall clearly most powerful classifier, and show that in most cases, a reduction of the dimensionality to two or three dimensions prior to classification increases the error in allocating test observations.
Journal ArticleDOI
Classifier design with incomplete knowledge
TL;DR: A new criterion function is proposed which approximates the error probability when the M classes do not span the entire pattern space, based on the probabilistic measures obtained from a modified version of Dubuisson and Masson's statistical decision rule with reject.
Journal ArticleDOI
Complex Contourlet-CNN for polarimetric SAR image classification
TL;DR: Experiments on different spatial resolutions and land coverings of Flevoland, San Francisco Bay, and Germany PolSAR images show that less training data is required and the performance of the proposed explainable deep learning method is comparable to that of the existing state-of-the-art methods.
References
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Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Book
Probability theory
TL;DR: These notes cover the basic definitions of discrete probability theory, and then present some results including Bayes' rule, inclusion-exclusion formula, Chebyshev's inequality, and the weak law of large numbers.
Journal ArticleDOI
On the mean accuracy of statistical pattern recognizers
TL;DR: The overall mean recognition probability (mean accuracy) of a pattern classifier is calculated and numerically plotted as a function of the pattern measurement complexity n and design data set size m, using the well-known probabilistic model of a two-class, discrete-measurement pattern environment.
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