Publikationsansicht

Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods (2007)

Abstract
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.

Details der Publikation
Download http://edoc.mpg.de/352334
Archiv Max Planck Society - eDocument Server (Germany)
Typ Conference-Paper