Adaptive Classification by Neural Net Based Prototype Populations

Peleg, Kalman; Ben-Hanan, Uri

doi:10.1142/9789812797926_0016

Cited by 1 publication

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“…., (1,1,1). * Within the Mean-Field BDRA all training and test data are assumed discrete, which means that each measurement, or feature, can take on one of a finite number of values defining its cardinality.…”

Section: Introductionmentioning

confidence: 99%

<title>Utilizing a class labeling feature in an adaptive Bayesian classifier</title>

Lynch

Willett²

2001

SPIE Proceedings

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In this paper, the Mean-Field Bayesian Data Reduction Algorithm is developed that adaptively trains on data containing missing values. In the basic data model for this algorithm each feature vector of a given class contains a class-labeling feature. Thus, the methods developed here are used to demonstrate performance for problems in which it is desired to adapt the existing training data with data containing missing values, such as the classlabeling feature. Given that, the Mean-Field Bayesian Data Reduction Algorithm labels the adapted data, while simultaneously determining those features that provide best classification performance. That is, performance is improved by reducing the data to mitigate the effects of the curse of dimensionality. Further, to demonstrate performance, the algorithm is compared to the classifier that does not adapt and bases its decisions on only the prior training data, and also the optimal clairvoyant classifier. *A5 an alternative, Bayesian techniques have also been developed for unsupervised training, and some typical examples of this can be found in.1'2 tIn all results shown here N = 1, meaning that only one test observation is tested at a time.

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Section: Introductionmentioning

confidence: 99%