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» Random Projection, Margins, Kernels, and Feature-Selection
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SLSFS
2005
Springer
12 years 9 hour 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...
Avrim Blum
ESANN
2006
11 years 8 months ago
Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
Long Han, Mark J. Embrechts, Boleslaw K. Szymanski...
ALT
2007
Springer
12 years 3 months ago
Learning Kernel Perceptrons on Noisy Data Using Random Projections
In this paper, we address the issue of learning nonlinearly separable concepts with a kernel classifier in the situation where the data at hand are altered by a uniform classific...
Guillaume Stempfel, Liva Ralaivola
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