Distributed optimization of multi-class SVMs.
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, whic...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2017-01-01
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Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC5453486?pdf=render |