Pages that link to "Item:Q1884603"
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The following pages link to Statistical behavior and consistency of classification methods based on convex risk minimization. (Q1884603):
Displaying 50 items.
- Fast structured prediction using large margin sigmoid belief networks (Q1931609) (← links)
- Calibrated asymmetric surrogate losses (Q1950846) (← links)
- Oracle inequalities for cross-validation type procedures (Q1950881) (← links)
- Upper bounds and aggregation in bipartite ranking (Q1951156) (← links)
- Random classification noise defeats all convex potential boosters (Q1959553) (← links)
- Surrogate losses in passive and active learning (Q2008623) (← links)
- Generalized Hadamard fractional integral inequalities for strongly \((s,m)\)-convex functions (Q2034516) (← links)
- SVM-boosting based on Markov resampling: theory and algorithm (Q2057733) (← links)
- Sparse classification: a scalable discrete optimization perspective (Q2071494) (← links)
- Nonregular and minimax estimation of individualized thresholds in high dimension with binary responses (Q2091840) (← links)
- InfoGram and admissible machine learning (Q2127228) (← links)
- A precise high-dimensional asymptotic theory for boosting and minimum-\(\ell_1\)-norm interpolated classifiers (Q2148995) (← links)
- Learning rates of kernel-based robust classification (Q2157879) (← links)
- Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric (Q2183591) (← links)
- Goal scoring, coherent loss and applications to machine learning (Q2191765) (← links)
- Learning rates for the kernel regularized regression with a differentiable strongly convex loss (Q2191832) (← links)
- Quantitative convergence analysis of kernel based large-margin unified machines (Q2191836) (← links)
- Learning with mitigating random consistency from the accuracy measure (Q2217414) (← links)
- Robust support vector machines based on the rescaled hinge loss function (Q2290317) (← links)
- Robustness of learning algorithms using hinge loss with outlier indicators (Q2292231) (← links)
- Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions (Q2313281) (← links)
- Generalization performance of Gaussian kernels SVMC based on Markov sampling (Q2339390) (← links)
- Online regularized learning with pairwise loss functions (Q2361154) (← links)
- Local Rademacher complexities and oracle inequalities in risk minimization. (2004 IMS Medallion Lecture). (With discussions and rejoinder) (Q2373576) (← links)
- The new interpretation of support vector machines on statistical learning theory (Q2379242) (← links)
- Fully online classification by regularization (Q2381648) (← links)
- Statistical performance of support vector machines (Q2426613) (← links)
- Ranking and empirical minimization of \(U\)-statistics (Q2426626) (← links)
- Recursive aggregation of estimators by the mirror descent algorithm with averaging (Q2432961) (← links)
- The asymptotics of ranking algorithms (Q2438754) (← links)
- On the consistency of multi-label learning (Q2446585) (← links)
- Convergence analysis of online algorithms (Q2454719) (← links)
- Simultaneous adaptation to the margin and to complexity in classification (Q2456017) (← links)
- Optimal rates of aggregation in classification under low noise assumption (Q2469663) (← links)
- On the rate of convergence for multi-category classification based on convex losses (Q2475308) (← links)
- Approximation with polynomial kernels and SVM classifiers (Q2498387) (← links)
- Optimal convex error estimators for classification (Q2498665) (← links)
- Learning rates of gradient descent algorithm for classification (Q2519710) (← links)
- Complexities of convex combinations and bounding the generalization error in classification (Q2583410) (← links)
- Boosting with early stopping: convergence and consistency (Q2583412) (← links)
- Classifiers of support vector machine type with \(\ell_1\) complexity regularization (Q2642804) (← links)
- Averaging versus voting: a comparative study of strategies for distributed classification (Q2668577) (← links)
- Robust minium bias iteration algorithms for classification ratemaking and loss reserving (Q2680661) (← links)
- Supervised Learning by Support Vector Machines (Q2789826) (← links)
- On the characterization of a class of Fisher-consistent loss functions and its application to boosting (Q2810880) (← links)
- Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory (Q2875744) (← links)
- ERM learning algorithm for multi-class classification (Q2909368) (← links)
- On the properties of variational approximations of Gibbs posteriors (Q2958606) (← links)
- Rademacher Chaos Complexities for Learning the Kernel Problem (Q3057230) (← links)
- An Algorithm for Unconstrained Quadratically Penalized Convex Optimization (Q3087581) (← links)
