 | Figure 1
Nature Biotechnology
- 24, 1565 - 1567 (2006)
doi:10.1038/nbt1206-1565
What is a support vector machine?William S Noble | | | | Figure 1. Support vector machines (SVMs) at work.
(a) Two-dimensional expression profiles of lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) samples. Each dimension corresponds to the measured mRNA expression level of a given gene. The SVM's task is to assign a label to the gene expression profile labeled 'Unknown'. (b) A separating hyperplane. Based upon this hyperplane, the inferred label of the 'Unknown' expression profile is 'ALL'. (c) A hyperplane in one dimension. The hyperplane is shown as a single black point. (d) A hyperplane in three dimensions. (e) Many possible separating hyperplanes. (f) The maximum-margin hyperplane. The three support vectors are circled. (g) A data set containing one error, indicated by arrow. (h) A separating hyperplane with a soft margin. Error is indicated by arrow. (i) A nonseparable one-dimensional data set. (j) Separating previously nonseparable data. (k) A linearly nonseparable two-dimensional data set, which is linearly separable in four dimensions. (l) An SVM that has overfit a two-dimensional data set. In a, b, d–h, the expression values are divided by 1,000.
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