Abstract
Using techniques from Statistical Physics, the annealed VC entropy for hyperplanes in high dimensional spaces is calculated as a function of the margin for a spherical Gaussian distribution of inputs.
Original language | English |
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Title of host publication | Advances in Kernel Methods - Support vector learning |
Editors | Bernhard Scholkopf, Christopher J. C. Burges, Alexander J. Smola |
Place of Publication | Cambridge, MA |
Publisher | MIT |
Pages | 117-126 |
Number of pages | 10 |
ISBN (Print) | 0262194163 |
Publication status | Published - 18 Dec 1998 |
Bibliographical note
Copyright of the Massachusetts Institute of Technology Press (MIT Press) Available in Google BooksKeywords
- annealed VC entropy
- hyperplanes
- high dimensional spaces
- spherical Gaussian distribution