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JMLR
2008

Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods

9 years 3 months ago
Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods
We propose a highly efficient framework for penalized likelihood kernel methods applied to multiclass models with a large, structured set of classes. As opposed to many previous approaches which try to decompose the fitting problem into many smaller ones, we focus on a Newton optimization of the complete model, making use of model structure and linear conjugate gradients in order to approximate Newton search directions. Crucially, our learning method is based entirely on matrix-vector multiplication primitives with the kernel matrices and their derivatives, allowing straightforward specialization to new kernels, and focusing code optimization efforts to these primitives only. Kernel parameters are learned automatically, by maximizing the cross-validation log likelihood in a gradient-based way, and predictive probabilities are estimated. We demonstrate our approach on large scale text classification tasks with hierarchical structure on thousands of classes, achieving state-of-the-art r...
Matthias W. Seeger
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where JMLR
Authors Matthias W. Seeger
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