Search results for "Prototype Reduction Schemes"

showing 4 items of 4 documents

The fundamental theory of optimal "Anti-Bayesian" parametric pattern classification using order statistics criteria

2013

Author's version of an article in the journal: Pattern Recognition. Also available from the publisher at: http://dx.doi.org/10.1016/j.patcog.2012.07.004 The gold standard for a classifier is the condition of optimality attained by the Bayesian classifier. Within a Bayesian paradigm, if we are allowed to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the (Mahalanobis) distance from the corresponding means. The reader should observe that, in this context, the mean, in one sense, is the most central point in the respective distribution. In this paper, we shall show that we can obtain opti…

Mahalanobis distanceVDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412Feature vectorOrder statisticBayesian probabilityclassification by moments of order statistics020206 networking & telecommunicationsVDP::Technology: 500::Information and communication technology: 55002 engineering and technologyprototype reduction schemesNaive Bayes classifierBayes' theoremExponential familypattern classificationorder statisticsArtificial IntelligenceSignal Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionAlgorithmSoftwarereduction of training patternsMathematicsParametric statistics
researchProduct

Ultimate Order Statistics-Based Prototype Reduction Schemes

2013

Published version of a chapter in the book: AI 2013: Advances in Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-319-03680-9_42 The objective of Prototype Reduction Schemes (PRSs) and Border Identification (BI) algorithms is to reduce the number of training vectors, while simultaneously attempting to guarantee that the classifier built on the reduced design set performs as well, or nearly as well, as the classifier built on the original design set. In this paper, we shall push the limit on the field of PRSs to see if we can obtain a classification accuracy comparable to the optimal, by condensing the information in the data set into a single tr…

Training setComputer scienceVDP::Mathematics and natural science: 400::Information and communication science: 420::Algorithms and computability theory: 422Order statisticcomputer.software_genreSupport vector machineData setBayes' theoremclassification using Order Statistics (OS)CMOSPrototype Reduction SchemesData miningmoments of OSClassifier (UML)computerParametric statistics
researchProduct

“Anti-Bayesian” parametric pattern classification using order statistics criteria for some members of the exponential family

2013

This paper submits a comprehensive report of the use of order statistics (OS) for parametric pattern recognition (PR) for various distributions within the exponential family. Although the field of parametric PR has been thoroughly studied for over five decades, the use of the OS of the distributions to achieve this has not been reported. The pioneering work on using OS for classification was presented earlier for the uniform distribution and for some members of the exponential family, where it was shown that optimal PR can be achieved in a counter-intuitive manner, diametrically opposed to the Bayesian paradigm, i.e., by comparing the testing sample to a few samples distant from the mean. A…

Uniform distribution (continuous)classification by moments of order statisticsBayesian probabilityOrder statisticNonparametric statisticsVDP::Technology: 500::Information and communication technology: 550020206 networking & telecommunications02 engineering and technologyprototype reduction schemesBayes' theorempattern classificationVDP::Mathematics and natural science: 400::Information and communication science: 420Exponential familyArtificial IntelligenceSignal Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionBeta distributionAlgorithmSoftwareMathematicsParametric statisticsPattern Recognition
researchProduct

On using prototype reduction schemes to optimize locally linear reconstruction methods

2012

Authors version of an article published in the journal: Pattern Recognition. Also available from the publisher at: http://dx.doi.org/10.1016/j.patcog.2011.06.021 This paper concerns the use of prototype reduction schemes (PRS) to optimize the computations involved in typical k-nearest neighbor (k-NN) rules. These rules have been successfully used for decades in statistical pattern recognition (PR) [1,15] applications and are particularly effective for density estimation, classification, and regression because of the known error bounds that they possess. For a given data point of unknown identity, the k-NN possesses the phenomenon that it combines the information about the samples from a pri…

VDP::Mathematics and natural science: 400::Information and communication science: 420::Knowledge based systems: 425prototype reduction schemes (PRS)VDP::Technology: 500::Information and communication technology: 550k-nearest neighbor (k−NN) learninglocally linear reconstruction (LLR)
researchProduct