KODAMA (1.5)

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Knowledge Discovery by Accuracy Maximization.


KODAMA algorithm is an unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. The algorithm was published by Cacciatore et al. 2014 . Addition functions was introduced by Cacciatore et al. 2017 to facilitate the identification of key features associated with the generated output and are easily interpretable for the user. Cross-validated techniques are also included in this package.

Maintainer: Stefano Cacciatore
Author(s): Stefano Cacciatore, Leonardo Tenori, Claudio Luchinat, Phillip R. Bennett, and David A. MacIntyre

License: GPL (>= 2)

Uses: Rcpp, rgl, knitr, rmarkdown

Released about 1 year ago.

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