Pages that link to "Item:Q1884602"

The following pages link to On the Bayes-risk consistency of regularized boosting methods. (Q1884602):

Displaying 50 items.

• Universally consistent vertex classification for latent positions graphs (Q366983) (← links)
• Approximation by multivariate Bernstein-Durrmeyer operators and learning rates of least-squares regularized regression with multivariate polynomial kernels (Q390534) (← links)
• A simple extension of boosting for asymmetric mislabeled data (Q419240) (← links)
• Further results on the margin explanation of boosting: new algorithm and experiments (Q439827) (← links)
• Two oracle inequalities for regularized boosting classifiers (Q440099) (← links)
• Boosting algorithms: regularization, prediction and model fitting (Q449780) (← links)
• Fully corrective boosting with arbitrary loss and regularization (Q460675) (← links)
• Does modeling lead to more accurate classification? A study of relative efficiency in linear classification (Q476236) (← links)
• Cox process functional learning (Q500875) (← links)
• Boosting conditional probability estimators (Q513339) (← links)
• Learning rates for multi-kernel linear programming classifiers (Q537615) (← links)
• Density estimation by the penalized combinatorial method (Q558002) (← links)
• Component-wisely sparse boosting (Q743775) (← links)
• Soft-max boosting (Q747255) (← links)
• On the accuracy of cross-validation in the classification problem (Q832978) (← links)
• Tutorial series on brain-inspired computing. VI: Geometrical structure of boosting algorithm (Q857990) (← links)
• Best subset selection, persistence in high-dimensional statistical learning and optimization under \(l_1\) constraint (Q869974) (← links)
• Multi-kernel regularized classifiers (Q870343) (← links)
• Regularization in statistics (Q882931) (← links)
• Boosting and instability for regression trees (Q959181) (← links)
• New multicategory boosting algorithms based on multicategory Fisher-consistent losses (Q999662) (← links)
• On surrogate loss functions and \(f\)-divergences (Q1020983) (← links)
• Convergence rates of generalization errors for margin-based classification (Q1021988) (← links)
• On boosting kernel regression (Q1031760) (← links)
• Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients (Q1615281) (← links)
• Bootstrap -- an exploration (Q1731214) (← links)
• Deformation of log-likelihood loss function for multiclass boosting (Q1784701) (← links)
• Population theory for boosting ensembles. (Q1884600) (← links)
• Statistical behavior and consistency of classification methods based on convex risk minimization. (Q1884603) (← links)
• Asymptotic convergence of dimension reduction based boosting in classification (Q1931355) (← links)
• Calibrated asymmetric surrogate losses (Q1950846) (← links)
• Classification with minimax fast rates for classes of Bayes rules with sparse representation (Q1951772) (← links)
• Random classification noise defeats all convex potential boosters (Q1959553) (← links)
• Multiclass classification, information, divergence and surrogate risk (Q1990579) (← links)
• SVM-boosting based on Markov resampling: theory and algorithm (Q2057733) (← links)
• Nonregular and minimax estimation of individualized thresholds in high dimension with binary responses (Q2091840) (← links)
• AdaBoost and robust one-bit compressed sensing (Q2102435) (← links)
• A precise high-dimensional asymptotic theory for boosting and minimum-\(\ell_1\)-norm interpolated classifiers (Q2148995) (← links)
• Aggregation of estimators and stochastic optimization (Q2197367) (← links)
• Double machine learning with gradient boosting and its application to the Big \(N\) audit quality effect (Q2305992) (← links)
• On the interpretation of ensemble classifiers in terms of Bayes classifiers (Q2317169) (← links)
• Local Rademacher complexities and oracle inequalities in risk minimization. (2004 IMS Medallion Lecture). (With discussions and rejoinder) (Q2373576) (← links)
• Fully online classification by regularization (Q2381648) (← links)
• Accelerated gradient boosting (Q2425242) (← links)
• Ranking and empirical minimization of \(U\)-statistics (Q2426626) (← links)
• Recursive aggregation of estimators by the mirror descent algorithm with averaging (Q2432961) (← 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)
• Learning gradients by a gradient descent algorithm (Q2480334) (← links)