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.

• Noise peeling methods to improve boosting algorithms (Q1660240) (← links)
• Probing for sparse and fast variable selection with model-based boosting (Q1664500) (← links)
• An update on statistical boosting in biomedicine (Q1664502) (← links)
• Probability estimation for multi-class classification using adaboost (Q1677008) (← links)
• Modeling threshold interaction effects through the logistic classification trunk (Q1695093) (← links)
• Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates (Q1703866) (← links)
• Pathway-based kernel boosting for the analysis of genome-wide association studies (Q1705355) (← links)
• Covariate balancing propensity score by tailored loss functions (Q1731067) (← links)
• Bootstrap -- an exploration (Q1731214) (← links)
• Boosting-based sequential output prediction (Q1758664) (← links)
• Cost-sensitive learning and decision making revisited (Q1779549) (← links)
• Bandwidth choice for nonparametric classification (Q1781162) (← links)
• Deformation of log-likelihood loss function for multiclass boosting (Q1784701) (← links)
• Robust estimation and empirical likelihood inference with exponential squared loss for panel data models (Q1787341) (← links)
• BoostWofE: a new sequential weights of evidence model reducing the effect of conditional dependency (Q1789094) (← links)
• On weak base hypotheses and their implications for boosting regression and classification (Q1848929) (← links)
• Top-down decision tree learning as information based boosting (Q1870539) (← links)
• Least angle regression. (With discussion) (Q1879940) (← 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)
• Multilogistic regression by means of evolutionary product-unit neural networks (Q1932055) (← links)
• Forecasting with many predictors: is boosting a viable alternative? (Q1942870) (← links)
• Semiparametric regression during 2003--2007 (Q1952023) (← links)
• Random classification noise defeats all convex potential boosters (Q1959553) (← links)
• Improved boosting algorithms using confidence-rated predictions (Q1969321) (← links)
• A comparative study of the leading machine learning techniques and two new optimization algorithms (Q1991232) (← links)
• Calibrating AdaBoost for phoneme classification (Q2001122) (← links)
• Big data analytics for seismic fracture identification using amplitude-based statistics (Q2009869) (← links)
• Robust estimation for the varying coefficient partially nonlinear models (Q2012585) (← links)
• Automatic gait classification patterns in spastic hemiplegia (Q2022500) (← links)
• Learning causal effect using machine learning with application to China's typhoon (Q2023742) (← links)
• Isotonic boosting classification rules (Q2036157) (← links)
• Stochastic approximation: from statistical origin to big-data, multidisciplinary applications (Q2038304) (← links)
• Boosting high dimensional predictive regressions with time varying parameters (Q2043255) (← links)
• SVM-boosting based on Markov resampling: theory and algorithm (Q2057733) (← links)
• Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data (Q2066785) (← links)
• A likelihood-based boosting algorithm for factor analysis models with binary data (Q2076167) (← links)
• Adaptive step-length selection in gradient boosting for Gaussian location and scale models (Q2095757) (← links)
• AdaBoost and robust one-bit compressed sensing (Q2102435) (← links)
• Machine learning for corporate default risk: multi-period prediction, frailty correlation, loan portfolios, and tail probabilities (Q2103037) (← links)
• A new accelerated proximal boosting machine with convergence rate \(O(1/t^2)\) (Q2103099) (← links)
• Robust MAVE for single-index varying-coefficient models (Q2111967) (← links)
• A precise high-dimensional asymptotic theory for boosting and minimum-\(\ell_1\)-norm interpolated classifiers (Q2148995) (← links)
• Multilayer bootstrap networks (Q2179833) (← links)
• Goal scoring, coherent loss and applications to machine learning (Q2191765) (← links)
• Quantitative convergence analysis of kernel based large-margin unified machines (Q2191836) (← links)
• Using LogitBoost classifier to predict protein structural classes (Q2194896) (← links)
• Local uncertainty sampling for large-scale multiclass logistic regression (Q2196246) (← links)