wine: Wine data set.
In DavidHofmeyr/spuds: Spectral Partitioning Using Density Separation
Description
Usage
Format
Source
References
Description
This data set contains chemical compositions of 178 wines from three different origins.
Usage
1
Format
A list with entries $x (a 178x13 matrix with each row corresponding to an individual wine) and $c (a vector of region labels)
Source
UCI Machine Learning Repository.
References
Dheeru, D. and E. Karra Taniskidou (2017). UCI machine learning repository. https://archive.ics.uci.edu/ml
S. Aeberhard, D. Coomans and O. de Vel,
Comparison of Classifiers in High Dimensional Settings,
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
S. Aeberhard, D. Coomans and O. de Vel,
"THE CLASSIFICATION PERFORMANCE OF RDA"
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
DavidHofmeyr/spuds documentation built on May 20, 2019, 9:40 a.m.
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Description Usage Format Source References
Description
This data set contains chemical compositions of 178 wines from three different origins.
Usage
1 |
Format
A list with entries $x (a 178x13 matrix with each row corresponding to an individual wine) and $c (a vector of region labels)
Source
UCI Machine Learning Repository.
References
Dheeru, D. and E. Karra Taniskidou (2017). UCI machine learning repository. https://archive.ics.uci.edu/ml
S. Aeberhard, D. Coomans and O. de Vel, Comparison of Classifiers in High Dimensional Settings, Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland.
S. Aeberhard, D. Coomans and O. de Vel, "THE CLASSIFICATION PERFORMANCE OF RDA" Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland.
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