Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods
Abstract
We propose a highly efficient framework for penalized likelihood kernel methods applied to multi-class 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.
Cite
Text
Seeger. "Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods." Journal of Machine Learning Research, 2008.Markdown
[Seeger. "Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods." Journal of Machine Learning Research, 2008.](https://mlanthology.org/jmlr/2008/seeger2008jmlr-crossvalidation/)BibTeX
@article{seeger2008jmlr-crossvalidation,
title = {{Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods}},
author = {Seeger, Matthias W.},
journal = {Journal of Machine Learning Research},
year = {2008},
pages = {1147-1178},
volume = {9},
url = {https://mlanthology.org/jmlr/2008/seeger2008jmlr-crossvalidation/}
}