NDLI: How well do discrete Bayesian methods represent a true model for each class?

Content Provider	IEEE Xplore Digital Library
Author	Lynch Jr., R.S. Willett, P.K.
Copyright Year	2005
Description	Author affiliation: Signal Process. Branch, Naval Undersea Warfare Center, Newport, RI, USA (Lynch, R.S., Jr.)
Abstract	In this paper, mathematical formulas are developed for analytically determining how well discrete Bayesian classification methods, such as the combined Bayes test (CBT), estimate probability mass functions for each class. In general, an analytical solution for this problem is computationally not trivial and solutions are typically given with empirically generated results. In this case, the actual analytical probability of error expression is given for a two-class problem that is a function of the number of data samples, the true cell probabilities for each class, and the number of discretized cells. Results are shown by plotting the difference between the actual and optimal error probabilities, versus the optimal probability of error, where it is demonstrated that, as expected, discrete classifier performance depends on the number of data samples for each class. However, interesting additional results are given where it is shown that actual classifier performance depends on both the true value of the optimal error probability, and the overall quantization of the data.
File Size	1212666
File Format	PDF
ISBN	0780392868
DOI	10.1109/ICIF.2005.1591884
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Testing Training data Error analysis Error probability Quantization Signal processing Signal analysis Statistical analysis Statistical distributions binomial distribution Quantized features feature or decision level data fusion probability of error
Content Type	Text
Resource Type	Article

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