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2015 | 24 |
Article title

Mixture of Metrics Optimization for Machine Learning Problems

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PL
Abstracts
PL
The selection of data representation and metric for a given data set is one of the most crucial problems in machine learning since it affects the results of classification and clustering methods. In this paper we investigate how to combine a various data representations and metrics into a single function which better reflects the relationships between data set elements than a single representation-metric pair. Our approach relies on optimizing a linear combination of selected distance measures with use of least square approximation. The application of our method for classification and clustering of chemical compounds seems to increase the accuracy of these methods.
Publisher
Year
Volume
24
Physical description
Dates
published
2015
online
06 - 07 - 2016
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References
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Publication order reference
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YADDA identifier
bwmeta1.element.ojs-issn-2083-8476-year-2015-volume-24-article-6337
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