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SLSFS
2005
Springer

Random Projection, Margins, Kernels, and Feature-Selection

11 years 11 months ago
Random Projection, Margins, Kernels, and Feature-Selection
Random projection is a simple technique that has had a number of applications in algorithm design. In the context of machine learning, it can provide insight into questions such as “why is a learning problem easier if data is separable by a large margin?” and “in what sense is choosing a kernel much like choosing a set of features?” This talk is
Avrim Blum
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where SLSFS
Authors Avrim Blum
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