Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods
In: Neural Information Processing Systems 2006, 4 Dec - 7 Dec 2006, Vancouver, CA.
We propose a highly efficient framework for kernel multi-class models with
a large and structured set of classes. Kernel parameters are learned
automatically by maximizing the cross-validation log likelihood, and
predictive probabilities are estimated. We demonstrate our
approach on large scale text classification tasks with hierarchical class
structure, achieving state-of-the-art results in an order of magnitude less
time than previous work.