Solving MultiClass Classification with LaRank
antoine Bordes, Leon Bottou, Patrick Gallinari and Jason Weston
In: ICML 2007, 20-24 June 2007, Corvallis, Oregon.
Optimization algorithms for large margin multiclass recognizers are often too costly to handle ambitious problems with structured outputs and exponential numbers of classes. Optimization algorithms that rely on the full gradient are not effective because, unlike the solution, the gradient is not sparse and is very large. The LaRank algorithm sidesteps this difficulty by relying on a randomized exploration inspired by the perceptron algorithm. We show that this approach is competitive with gradient based optimizers on simple multiclass problems. Furthermore, a single LaRank pass over the training examples delivers test error rates that are nearly as good as those of the ﬁnal solution.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Additional Information:||Best Student Paper Award|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||antoine Bordes|
|Deposited On:||11 February 2008|