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

Michael Izbicki
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):648-656, 2013.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-izbicki13, title = {Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training}, author = {Izbicki, Michael}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {648--656}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/izbicki13.pdf}, url = {https://proceedings.mlr.press/v28/izbicki13.html}, 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.} }
Endnote
%0 Conference Paper %T Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training %A Michael Izbicki %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-izbicki13 %I PMLR %P 648--656 %U https://proceedings.mlr.press/v28/izbicki13.html %V 28 %N 3 %X 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.
RIS
TY - CPAPER TI - Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training AU - Michael Izbicki BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/05/26 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-izbicki13 PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 3 SP - 648 EP - 656 L1 - http://proceedings.mlr.press/v28/izbicki13.pdf UR - https://proceedings.mlr.press/v28/izbicki13.html AB - 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. ER -
APA
Izbicki, M.. (2013). Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(3):648-656 Available from https://proceedings.mlr.press/v28/izbicki13.html.

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