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4 weeks ago
In 1992, Boser, Guyon, and Vapnik suggested a way to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. How does the resulting algorithm differ from the original optimal hyperplane algorithm proposed by Vladimir Vapnik in 1963?
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Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support


Edition: 11th
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4 weeks ago
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The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in the transformed feature space. The transformation may be nonlinear and the transformed space high dimensional; thus, though the classifier is a hyperplane in the high-dimensional feature space it may be nonlinear in the original input space.
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4 weeks ago
Brilliant
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