LaRank, SGD-QN - Fast Optimizers for Linear SVM
published: Sept. 1, 2008, recorded: July 2008, views: 6395
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Originally proposed for solving multiclass SVM, the LaRank algorithm is a dual coordinate ascent algorithm relying on a randomized exploration inspired by the perceptron algorithm [Bordes05, Bordes07]. This approach is competitive with gradient based optimizers on simple binary and multiclass problems. Furthermore, very few LaRank passes over the training examples delivers test error rates that are nearly as good as those of the final solution. For this entry we ran several epochs of the LaRank algorithm until reaching the convergence criterion.
The SGD-QN algorithm uses stochastic gradient descent modified using an efficient method to estimate the diagonal of the inverse Hessian. The estimation method is inspired oLBFGS [Schraudolph, 07]. Since there is a little need to update this estimated matrix at each iteration, this approximate second-order stochastic gradient method iterates nearly as fast than a classical stochastic gradient descent [Bottou98, Bottou07] but requires less iterations.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !