Pages that link to "Item:Q1807115"

The following pages link to Arcing classifiers. (With discussion) (Q1807115):

Displaying 50 items.

• Bounding the generalization error of convex combinations of classifiers: Balancing the dimensionality and the margins. (Q1872344) (← links)
• Generalization error of combined classifiers. (Q1872713) (← links)
• Population theory for boosting ensembles. (Q1884600) (← links)
• Process consistency for AdaBoost. (Q1884601) (← links)
• On the Bayes-risk consistency of regularized boosting methods. (Q1884602) (← links)
• Statistical behavior and consistency of classification methods based on convex risk minimization. (Q1884603) (← links)
• Semiparametric regression during 2003--2007 (Q1952023) (← links)
• Statistical uncertainty estimation using random forests and its application to drought forecast (Q1955344) (← links)
• Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change (Q2029637) (← links)
• A distributed algorithm for high-dimension convex quadratically constrained quadratic programs (Q2057223) (← links)
• SVM-boosting based on Markov resampling: theory and algorithm (Q2057733) (← links)
• AdaBoost and robust one-bit compressed sensing (Q2102435) (← links)
• A new accelerated proximal boosting machine with convergence rate \(O(1/t^2)\) (Q2103099) (← links)
• Comparing boosting and bagging for decision trees of rankings (Q2129304) (← links)
• A precise high-dimensional asymptotic theory for boosting and minimum-\(\ell_1\)-norm interpolated classifiers (Q2148995) (← links)
• Using LogitBoost classifier to predict protein structural classes (Q2194896) (← links)
• Machine learning acceleration for nonlinear solvers applied to multiphase porous media flow (Q2237483) (← links)
• Canonical forest (Q2259759) (← links)
• Boosting as a kernel-based method (Q2331677) (← links)
• Classification by evolutionary ensembles (Q2369589) (← links)
• Density estimation with stagewise optimization of the empirical risk (Q2384149) (← links)
• Quadratic boosting (Q2384982) (← links)
• Quantum adiabatic machine learning (Q2393692) (← links)
• Accelerated gradient boosting (Q2425242) (← links)
• Assessing the stability of classification trees using Florida birth data (Q2455414) (← 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)
• Classifying G-protein coupled receptors with bagging classification tree (Q2490541) (← links)
• Boosting for high-dimensional linear models (Q2497175) (← links)
• Diversification for better classification trees (Q2499153) (← links)
• On the fusion of threshold classifiers for categorization and dimensionality reduction (Q2513348) (← links)
• Time series forecasting with multiple candidate models: selecting or combining? (Q2583096) (← links)
• Complexities of convex combinations and bounding the generalization error in classification (Q2583410) (← links)
• Boosting with early stopping: convergence and consistency (Q2583412) (← links)
• Attractor networks for shape recognition (Q2723314) (← links)
• Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure (Q2775812) (← links)
• Quantum AdaBoost algorithm via cluster state (Q2978340) (← links)
• Application of “Aggregated Classifiers” in Survival Time Studies (Q3298669) (← links)
• Bagging Tree Classifiers for Glaucoma Diagnosis (Q3298671) (← links)
• Theory of Classification: a Survey of Some Recent Advances (Q3373749) (← links)
• New aspects of Bregman divergence in regression and classification with parametric and nonparametric estimation (Q3636244) (← links)
• A Bayesian Random Split to Build Ensembles of Classification Trees (Q3638170) (← links)
• Aggregating classifiers with ordinal response structure (Q4675843) (← links)
• Delta Boosting Machine with Application to General Insurance (Q4689973) (← links)
• Boosting with Noisy Data: Some Views from Statistical Theory (Q4819816) (← links)
• Different Paradigms for Choosing Sequential Reweighting Algorithms (Q4819817) (← links)
• ON MATHEMATICAL MODELLING OF SYNTHETIC MEASURES (Q4959420) (← links)
• Boosting in the Presence of Outliers: Adaptive Classification With Nonconvex Loss Functions (Q4962433) (← links)
• (Q4969173) (← links)