Pages that link to "Item:Q1848780"

The following pages link to Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors) (Q1848780):

Displaying 50 items.

• Robust variable selection with exponential squared loss for the spatial autoregressive model (Q829731) (← links)
• Cost-sensitive ensemble learning: a unifying framework (Q832635) (← links)
• On the accuracy of cross-validation in the classification problem (Q832978) (← links)
• Representation in the (artificial) immune system (Q839513) (← links)
• Logistic regression using covariates obtained by product-unit neural network models (Q853094) (← links)
• Tutorial series on brain-inspired computing. VI: Geometrical structure of boosting algorithm (Q857990) (← links)
• Improved customer choice predictions using ensemble methods (Q872292) (← links)
• Self-improved gaps almost everywhere for the agnostic approximation of monomials (Q884469) (← links)
• A Fisher consistent multiclass loss function with variable margin on positive examples (Q887268) (← links)
• Boosting multi-features with prior knowledge for mini unmanned helicopter landmark detection (Q926121) (← links)
• Counting and enumerating aggregate classifiers (Q955312) (← links)
• Soft memberships for spectral clustering, with application to permeable language distinction (Q955819) (← links)
• An extensive comparison of recent classification tools applied to microarray data (Q957169) (← links)
• Boosting and instability for regression trees (Q959181) (← links)
• Boosting additive models using component-wise P-splines (Q961113) (← links)
• Using boosting to prune double-bagging ensembles (Q961263) (← links)
• Additive prediction and boosting for functional data (Q961283) (← links)
• Boosting nonlinear additive autoregressive time series (Q961660) (← links)
• Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography (Q961895) (← links)
• A cascade of boosted generative and discriminative classifiers for vehicle detection (Q966898) (← links)
• Navigating random forests and related advances in algorithmic modeling (Q975577) (← links)
• Boosting GARCH and neural networks for the prediction of heteroskedastic time series (Q984159) (← links)
• Cost-sensitive boosting for classification of imbalanced data (Q996413) (← links)
• Functional dissipation microarrays for classification (Q996417) (← links)
• New multicategory boosting algorithms based on multicategory Fisher-consistent losses (Q999662) (← links)
• Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms (Q1006921) (← links)
• Sketching information divergences (Q1009269) (← links)
• Boosted Bayesian network classifiers (Q1009291) (← links)
• Exact bootstrap \(k\)-nearest neighbor learners (Q1009331) (← links)
• Surrogate maximization/minimization algorithms and extensions (Q1009342) (← links)
• A dynamic model of expected bond returns: A functional gradient descent approach (Q1010570) (← links)
• Parallelizing AdaBoost by weights dynamics (Q1019879) (← links)
• Boosting ridge regression (Q1020707) (← links)
• A stochastic approximation view of boosting (Q1020818) (← links)
• A local boosting algorithm for solving classification problems (Q1023522) (← links)
• Logitboost with errors-in-variables (Q1023585) (← links)
• A \(\mathbb R\)eal generalization of discrete AdaBoost (Q1028894) (← links)
• On boosting kernel regression (Q1031760) (← links)
• Embedding ensemble tracking in a stochastic framework for robust object tracking (Q1049841) (← links)
• Estimating the dimension of a model (Q1247128) (← links)
• Some relationships between fuzzy and random set-based classifiers and models (Q1347873) (← links)
• Complexity in the case against accuracy estimation (Q1399985) (← links)
• Vote counting measures for ensemble classifiers. (Q1425964) (← links)
• A fast genetic method for inducting descriptive fuzzy models. (Q1430850) (← links)
• Statistical modeling: The two cultures. (With comments and a rejoinder). (Q1431204) (← links)
• A concrete statistical realization of Kleinberg's stochastic dicrimination for pattern recognition. I: Two-class classification (Q1431432) (← links)
• A geometric approach to leveraging weak learners (Q1603593) (← links)
• Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients (Q1615281) (← links)
• Logitboost autoregressive networks (Q1654262) (← links)
• Gradient boosting for high-dimensional prediction of rare events (Q1658126) (← links)