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Article Dans Une Revue Nature Methods Année : 2018

Machine learning: Supervised methods, SVM and kNN

Résumé

In supervised learning, a set of input variables, such as blood metabolite or gene expression levels, are used to predict a quantitative response variable like hormone level or a qualitative one such as healthy versus diseased individuals. We have previously discussed several supervised learning algorithms, including logistic regression and random forests, and their typical behaviors with different sample sizes and numbers of predictor variables. This month, we look at two very common supervised methods in the context of machine learning: linear support vector machines (SVM) and k-nearest neighbors (kNN). Both have been successfully applied to challenging pattern-recognition problems in biology and medicine.
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Dates et versions

hal-01657491 , version 1 (06-12-2017)

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  • HAL Id : hal-01657491 , version 1

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Danilo Bzdok, Martin Krzywinski, Naomi Altman. Machine learning: Supervised methods, SVM and kNN. Nature Methods, 2018, pp.1-6. ⟨hal-01657491⟩
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