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.

• A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates (Q2218385) (← links)
• An incremental aggregated proximal ADMM for linearly constrained nonconvex optimization with application to sparse logistic regression problems (Q2226322) (← links)
• Instance-dependent cost-sensitive learning for detecting transfer fraud (Q2242220) (← links)
• Extending models via gradient boosting: an application to Mendelian models (Q2247455) (← links)
• Sample size determination for logistic regression (Q2252748) (← links)
• A combination selection algorithm on forecasting (Q2256180) (← links)
• Multinomial logit models with implicit variable selection (Q2256779) (← links)
• GA-Ensemble: a genetic algorithm for robust ensembles (Q2259225) (← links)
• Prediction and classification in nonlinear data analysis: something old, something new, something borrowed, something blue (Q2259888) (← links)
• Supervised projection approach for boosting classifiers (Q2270792) (← links)
• Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques (Q2270811) (← links)
• Local fractal and multifractal features for volumic texture characterization (Q2275965) (← links)
• Discriminative deep belief networks for visual data classification (Q2275979) (← links)
• Gender discriminating models from facial surface normals (Q2276027) (← links)
• Transformation boosting machines (Q2302476) (← links)
• On the interpretation of ensemble classifiers in terms of Bayes classifiers (Q2317169) (← links)
• Boosting as a kernel-based method (Q2331677) (← links)
• Logistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan households (Q2378359) (← links)
• Density estimation with stagewise optimization of the empirical risk (Q2384149) (← links)
• Multi-class learning by smoothed boosting (Q2384151) (← links)
• Quadratic boosting (Q2384982) (← links)
• Accelerated gradient boosting (Q2425242) (← links)
• Recursive aggregation of estimators by the mirror descent algorithm with averaging (Q2432961) (← links)
• An empirical comparison of learning algorithms for nonparametric scoring: the \textsc{TreeRank} algorithm and other methods (Q2444590) (← links)
• Boosting iterative stochastic ensemble method for nonlinear calibration of subsurface flow models (Q2449910) (← 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)
• An empirical study of using Rotation Forest to improve regressors (Q2470171) (← links)
• Analysis of boosting algorithms using the smooth margin function (Q2473080) (← links)
• An efficient modified boosting method for solving classification problems (Q2479397) (← links)
• Boosting for high-dimensional linear models (Q2497175) (← links)
• Multicategory large margin classification methods: hinge losses vs. coherence functions (Q2510115) (← links)
• An improved multiclass LogitBoost using adaptive-one-vs-one (Q2514757) (← links)
• Boosting with early stopping: convergence and consistency (Q2583412) (← links)
• Multi-class boosting with asymmetric binary weak-learners (Q2629847) (← links)
• Uncertainty and forecasts of U.S. recessions (Q2697092) (← links)
• Statistical Monitoring of Nominal Logistic Profiles in Phase II (Q2792265) (← links)
• Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of SIRTUIN6 (Q2805500) (← links)
• Designing a Boosted Classifier on Riemannian Manifolds (Q2807060) (← links)
• An extension of the receiver operating characteristic curve and AUC-optimal classification (Q2840869) (← links)
• Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory (Q2875744) (← links)
• Boosting in structured additive models. (Q2914098) (← links)
• Regression trees for predicting mortality in patients with cardiovascular disease: what improvement is achieved by using ensemble-based methods? (Q2919467) (← links)
• A calibrated multiclass extension of AdaBoost (Q2921170) (← links)
• Combining biomarkers to optimize patient treatment recommendations (Q2927626) (← links)
• Data Reduction Using a Discrete Wavelet Transform in Discriminant Analysis of Very High Dimensionality Data (Q3079089) (← links)
• AN ASYMMETRIC ADAPTIVE CLASSIFICATION METHOD (Q3084705) (← links)
• Three Categories Customer Churn Prediction Based on the Adjusted Real Adaboost (Q3102903) (← links)
• Stratified Normalization LogitBoost for Two-Class Unbalanced Data Classification (Q3102907) (← links)
• Nonparametric Decomposition of Time Series Data with Inputs (Q3168381) (← links)