0000000000471035

AUTHOR

Alioune Ngom

showing 2 related works from this author

A new paradigm for pattern classification: Nearest Border Techniques

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_44 There are many paradigms for pattern classification. As opposed to these, this paper introduces a paradigm that has not been reported in the literature earlier, which we shall refer to as the Nearest Border (NB) paradigm. The philosophy for developing such a NB strategy is as follows: Given the training data set for each class, we shall first attempt to create borders for each individual class. After that, we advocate that testing is accomplished by assigning the test sample to the class whose border it lies closest to…

Class (set theory)Training setPattern ClassificationComputer sciencebusiness.industrySVMVDP::Mathematics and natural science: 400::Information and communication science: 420::Algorithms and computability theory: 422Centroid02 engineering and technology01 natural sciencesVDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411Support vector machine010104 statistics & probabilityExperimental testingOutlier0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligence0101 mathematics10. No inequalitySet (psychology)businessTest sampleBorder Identification
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Pattern classification using a new border identification paradigm: The nearest border technique

2015

Abstract There are many paradigms for pattern classification such as the optimal Bayesian, kernel-based methods, inter-class border identification schemes, nearest neighbor methods, nearest centroid methods, among others. As opposed to these, this paper pioneers a new paradigm, which we shall refer to as the nearest border (NB) paradigm. The philosophy for developing such a NB strategy is as follows: given the training data set for each class, we shall attempt to create borders for each individual class. However, unlike the traditional border identification (BI) methods, we do not undertake this by using inter-class criteria; rather, we attempt to obtain the border for a specific class in t…

Class (set theory)Theoretical computer scienceComputer sciencebusiness.industryCognitive NeuroscienceCentroidComputer Science Applicationsk-nearest neighbors algorithmSet (abstract data type)Kernel (linear algebra)Identification (information)Artificial IntelligenceKernel (statistics)OutlierArtificial intelligencebusiness
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