Citation

BibTex format

@inproceedings{Joulani:2015,
author = {Joulani, P and Gyorgy, A and Szepesvari, C},
title = {Classification with Margin Constraints: A Unification with Applications to Optimization},
url = {http://opt-ml.org/oldopt/opt15/papers.html},
year = {2015}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - This paper introduces Classification with Margin Constraints (CMC), a simplegeneralization of cost-sensitive classification that unifies several learning settings.In particular, we show that a CMC classifier can be used, out of the box, to solveregression, quantile estimation, and several anomaly detection formulations. Onthe one hand, our reductions to CMC are at the loss level: the optimization problemto solve under the equivalent CMC setting is exactly the same as the optimizationproblem under the original (e.g. regression) setting. On the other hand,due to the close relationship between CMC and standard binary classification, theideas proposed for efficient optimization in binary classification naturally extendto CMC. As such, any improvement in CMC optimization immediately transfersto the domains reduced to CMC, without the need for new derivations or programs.To our knowledge, this unified view has been overlooked by the existingpractice in the literature, where an optimization technique (such as SMO or PEGASOS)is first developed for binary classification and then extended to otherproblem domains on a case-by-case basis. We demonstrate the flexibility of CMCby reducing two recent anomaly detection and quantile learning methods to CMC.
AU - Joulani,P
AU - Gyorgy,A
AU - Szepesvari,C
PY - 2015///
TI - Classification with Margin Constraints: A Unification with Applications to Optimization
UR - http://opt-ml.org/oldopt/opt15/papers.html
UR - http://hdl.handle.net/10044/1/40574
ER -