On Metric Correction and Conditionality of Raw Featureless Data in Machine Learning View Full Text


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Article Info

DATE

2018-10

AUTHORS

S. D. Dvoenko, D. O. Pshenichny

ABSTRACT

Recently, raw experimental data in machine learning often appear as direct comparisons between objects (featureless data). Different ways to evaluate difference or similarity of a pair of objects in image and data mining, image analysis, bioinformatics, etc., are usually used in practice. Nevertheless, such comparisons often are not distances or correlations (scalar products) like a correct function defined on a limited set of elements in machine learning. This problem is denoted as metric violations in ill-posed matrices. Therefore, it needs to recover violated metrics and provide optimal conditionality of corresponding matrices of pairwise comparisons for distances and similarities. This is the correct basis for using of modern machine learning algorithms. More... »

PAGES

595-604

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s1054661818040089

DOI

http://dx.doi.org/10.1134/s1054661818040089

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110428346


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