Pages that link to "Item:Q2893108"

The following pages link to Boosting. Foundations and algorithms. (Q2893108):

Displaying 50 items.

• Performance of empirical risk minimization in linear aggregation (Q282546) (← links)
• Two oracle inequalities for regularized boosting classifiers (Q440099) (← links)
• Boosting algorithms: regularization, prediction and model fitting (Q449780) (← links)
• Comment on: Boosting algorithms: regularization, prediction and model fitting (Q449783) (← links)
• Scheme of boosting in the problems of combinatorial optimization induced by the collective training algorithms (Q463372) (← links)
• Estimating the algorithmic variance of randomized ensembles via the bootstrap (Q666594) (← links)
• A support vector machine-based ensemble algorithm for breast cancer diagnosis (Q723956) (← links)
• Soft-max boosting (Q747255) (← links)
• Tutorial series on brain-inspired computing. VI: Geometrical structure of boosting algorithm (Q857990) (← links)
• Committee polyhedral separability: complexity and polynomial approximation (Q890319) (← links)
• Deep learning of support vector machines with class probability output networks (Q890735) (← links)
• A decision-theoretic generalization of on-line learning and an application to boosting (Q1370863) (← links)
• Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients (Q1615281) (← links)
• An update on statistical boosting in biomedicine (Q1664502) (← links)
• A robust AdaBoost.RT based ensemble extreme learning machine (Q1665122) (← links)
• High-dimensional time series prediction using kernel-based koopman mode regression (Q1696900) (← links)
• Gaussian-Gamma collaborative filtering: a hierarchical Bayesian model for recommender systems (Q1741490) (← links)
• Population theory for boosting ensembles. (Q1884600) (← links)
• Boosting a weak learning algorithm by majority (Q1899915) (← links)
• Multiclass classification, information, divergence and surrogate risk (Q1990579) (← links)
• Calibrating AdaBoost for phoneme classification (Q2001122) (← links)
• Learning causal effect using machine learning with application to China's typhoon (Q2023742) (← links)
• A likelihood-based boosting algorithm for factor analysis models with binary data (Q2076167) (← links)
• Handling concept drift via model reuse (Q2183593) (← links)
• Fast greedy \(\mathcal{C} \)-bound minimization with guarantees (Q2217455) (← links)
• Grafting for combinatorial binary model using frequent itemset mining (Q2218401) (← links)
• Feature extraction using conformal geometric algebra for AdaBoost algorithm based in-plane rotated face detection (Q2274732) (← links)
• Ensemble quantile classifier (Q2291292) (← links)
• Estimating a sharp convergence bound for randomized ensembles (Q2317334) (← links)
• Boosting as a kernel-based method (Q2331677) (← links)
• Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification (Q2660967) (← links)
• Labeled Compression Schemes for Extremal Classes (Q2830265) (← links)
• Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability prediction (Q3295738) (← links)
• ON PARAMETERIZED COMPLEXITY OF HITTING SET PROBLEM FOR AXIS–PARALLEL SQUARES INSTERSECTING A STRAIGHT LINE (Q4581438) (← links)
• Boosted KZ and LLL Algorithms (Q4621857) (← links)
• (Q4633011) (← links)
• Information Geometry of U-Boost and Bregman Divergence (Q4832497) (← links)
• Pruning variable selection ensembles (Q4970243) (← links)
• (Q5054632) (← links)
• Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications (Q5060788) (← links)
• Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate (Q5066399) (← links)
• Optimization of Tree Ensembles (Q5144785) (← links)
• A Data-Driven Random Subfeature Ensemble Learning Algorithm for Weather Forecasting (Q5162342) (← links)
• (Q5214229) (← links)
• Learning Theory (Q5473650) (← links)
• Multi-class ℓ2-Boost with the scoring coding (Q5506744) (← links)
• New two-level ensemble method and its application to chemical compounds properties prediction (Q6040357) (← links)
• Joint leaf-refinement and ensemble pruning through \(L_1\) regularization (Q6040512) (← links)
• Explainable subgradient tree boosting for prescriptive analytics in operations management (Q6087515) (← links)
• Synthetic learner: model-free inference on treatments over time (Q6163256) (← links)