WebMulticlass learning is an area of growing practical relevance, for which the currently available theory is still far from providing satisfactory understanding. We study the learnability of multiclass prediction, and derive upper and lower bounds on the sample complexity of multiclass hypothesis classes in different learning models: batch/online ... WebOur analysis reveals a surprising phenomenon: In the multiclass setting, in sharp contrast to binary classification, not all Empirical Risk Minimization (ERM) algorithms are …

Learning diverse and discriminative representations via the principle ...

WebWe introduce the notion of learning from contradictions, a.k.a Universum learning, for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We show that learning from contradictions (using MU-SVM) incurs lower sample complexity compared to multiclass SVM (M-SVM) by deriving the Natarajan dimension … Web30 dec. 2014 · Multiclass Learnability and the ERM principle. ... We propose a principle for designing good ERM learners, and use this principle to prove tight bounds on the sample complexity of learning {\em ... miley and bruno mars

[1302.1043] The price of bandit information in multiclass online ...

Web5 feb. 2013 · al. - Multiclass learnability and the erm principle). We apply these results to the class of $\gamma$-margin multiclass linear classifiers in $\reals^d$. We show that the bandit error rate of this class is $\tilde{\Theta}(\frac{ Y }{\gamma^2})$ in the realizable case and $\tilde{\Theta}(\frac{1}{\gamma}\sqrt{ Y T})$ in the agnostic case. This Web1 ian. 2011 · One of the significant obstacles that arises in the multi-class setting with infinite labels is that the Empirical Risk Minimization (ERM) rule ceases to be a learner [DSS14, … WebFor the PAC setting our analysis reveals a surprising phenomenon: In sharp contrast to binary classification, we show that there exist multiclass hypothesis classes for which … new york commercial feed registration