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ZENODO
Dataset . 2017
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2017
License: CC BY
Data sources: ZENODO
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Transformation Attributes From Gtzan Audio Database

Authors: Martina Naumovska;

Transformation Attributes From Gtzan Audio Database

Abstract

This data set was used in my master thesis "Reproducibility of Machine Learning results". On one hand, this dataset was used as an input in creating Support Vector Machine , Naive Bayes and Random Forest models in R, Python, RapidMiner and Weka in order to experiment the influence that different development environments have on reproducibility of machine learning results. Furthermore all the models implemented in all of previously mentioned environments were tested in different operating systems such as Windows, Linux and Mac OS.

Keywords

machine learning results, Reproducibility

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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