Topic

Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training

Resource type
Author/contributor
Title
Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training
Abstract
We use abstract algebra to derive new algorithms for fast cross-validation, online learning, and parallel learning. To use these algorithms on a classification model, we must show that the model has appropriate algebraic structure. It is easy to give algebraic structure to some models, and we do this explicitly for Bayesian classifiers and a novel variation of decision stumps called HomStumps. But not all classifiers have an obvious structure, so we introduce the Free HomTrainer. This can be used to give a "generic" algebraic structure to any classifier. We use the Free HomTrainer to give algebraic structure to bagging and boosting. In so doing, we derive novel online and parallel algorithms, and present the first fast cross-validation schemes for these classifiers.
Date
2013
Proceedings Title
ICML
Short Title
Algebraic classifiers
Library Catalog
Semantic Scholar
Extra
ZSCC: 0000013
Citation
Izbicki, M. (2013). Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training. In ICML.
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