Abstract
In this chapter we use the Wolfram Language’s machine learning to carry out classification of known datasets as an illustration of base capabilities. Examples include a Leukemia classification, and the Iris dataset classification across algorithms.
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References
Anderson, E.: The irises of the Gaspe Peninsula. Bull. Am. Iris Soc. 59, 2–5 (1935)
Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc. 97(457), 77–87 (2002)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
Libbrecht, M.W., Noble, W.S.: Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321 EP (2015)
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Mias, G. (2018). Machine Learning. In: Mathematica for Bioinformatics. Springer, Cham. https://doi.org/10.1007/978-3-319-72377-8_9
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DOI: https://doi.org/10.1007/978-3-319-72377-8_9
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