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Classifier Loss Function Design  
The machine learning problem of classifier design is studied from the perspective of probability elicitation, in statistics. This shows that the standard approach of proceeding from the specification of a loss, to the minimization of conditional risk is overly restrictive. It is shown that a better alternative is to start from the specification of a functional form for the minimum conditional risk, and derive the loss function. This has various consequences of practical interest, such as showing that 1) the widely adopted practice of relying on convex loss functions is unnecessary, and 2) many new losses can be derived for classification problems. These points are illustrated by the derivation of a number of novel Bayes consistent loss functions, some of which are not convex but do not compromise the computational tractability of classifier design. A number of algorithms custom tailored for specific classification problems are derived based on novel loss functions. These include classification algorithms for cost sensitive learning, robust outlier resistant classification and variable margin classification.  
Algorithms: 


Publications: 
CostSensitive Boosting. Hamed MasnadiShirazi and Nuno Vasconcelos IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32(2), 294, March 2010 . IEEE [ps] [pdf]
Variable margin losses for classifier design.
Risk minimization, probability elicitation, and costsensitive SVMs
On the Design of Robust Classifiers for Computer Vision.
On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost.
Asymmetric Boosting
 
Contact:  Nuno Vasconcelos, Hamed MasnadiShirazi 
Copyright @ 2007
www.svcl.ucsd.edu