Feature evaluation criteria and contextual decoding algorithms in statistical pattern recognition.
Feature evaluation criteria and contextual decoding algorithms in statistical pattern recognition.
Theoretical Comparison of a Class of Feature Selection Criteria in Pattern Recognition
IEEE Transactions on Computers
A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform
Pattern Recognition Letters
Comments on "On a New Class of Bounds on Bayes' Risk in Multihypothesis Pattern Recognition"
IEEE Transactions on Computers
On information and distance measures, error bounds, and feature selection
Information Sciences: an International Journal
Generalized error bounds in pattern recognition
Pattern Recognition Letters
A multiclass, k-NN approach to Bayes risk estimation
Pattern Recognition Letters
Estimation, learning, and adaptation: systems that improve with use
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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An important measure concerning the use of statistical decision schemes is the error probability associated with the decision rule. Several methods giving bounds on the error probability are presently available, but, most often, the bounds are loose. Those methods generally make use of so-cailed distances between statistical distributions. In this paper a new distance is proposed which permits tighter bounds to be set on the error probability of the Bayesian decision rule and which is shown to be closely related to several certainty or separability measures. Among these are the nearest neighbor error rate and the average conditional quadratic entropy of Vajda. Moreover, our distance bears much resemblance to the information theoretic concept of equivocation. This relationship is discussed. Comparison is made between the bounds on the Bayes risk obtained with the Bhattacharyya coefficient, the equivocation, and the new measure which we have named the Bayesian distance.