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JMLR
2006
107views more  JMLR 2006»
13 years 5 months ago
Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
The consistency of classification algorithm plays a central role in statistical learning theory. A consistent algorithm guarantees us that taking more samples essentially suffices...
Di-Rong Chen, Tao Sun
JMLR
2010
105views more  JMLR 2010»
13 years 9 days ago
Classification Methods with Reject Option Based on Convex Risk Minimization
In this paper, we investigate the problem of binary classification with a reject option in which one can withhold the decision of classifying an observation at a cost lower than t...
Ming Yuan, Marten H. Wegkamp
CORR
2008
Springer
133views Education» more  CORR 2008»
13 years 5 months ago
Estimating divergence functionals and the likelihood ratio by convex risk minimization
We develop and analyze M-estimation methods for divergence functionals and the likelihood ratios of two probability distributions. Our method is based on a non-asymptotic variatio...
XuanLong Nguyen, Martin J. Wainwright, Michael I. ...
COLT
2004
Springer
13 years 9 months ago
Oracle Bounds and Exact Algorithm for Dyadic Classification Trees
This paper introduces a new method using dyadic decision trees for estimating a classification or a regression function in a multiclass classification problem. The estimator is bas...
Gilles Blanchard, Christin Schäfer, Yves Roze...
KDD
2008
ACM
120views Data Mining» more  KDD 2008»
14 years 6 months ago
Multi-class cost-sensitive boosting with p-norm loss functions
We propose a family of novel cost-sensitive boosting methods for multi-class classification by applying the theory of gradient boosting to p-norm based cost functionals. We establ...
Aurelie C. Lozano, Naoki Abe