Pages that link to "Item:Q127532"

The following pages link to Greedy function approximation: A gradient boosting machine. (Q127532):

Displaying 50 items.

• A recommendation system for car insurance (Q2219620) (← links)
• Jobs runtime forecast for JSCC RAS supercomputers using machine learning methods (Q2225846) (← links)
• Structure learning for relational logistic regression: an ensemble approach (Q2238344) (← links)
• Forecasting bankruptcy using biclustering and neural network-based ensembles (Q2241078) (← links)
• A deep multitask learning approach for air quality prediction (Q2241161) (← links)
• To imprison or not to imprison: an analytics model for drug courts (Q2241165) (← links)
• Instance-dependent cost-sensitive learning for detecting transfer fraud (Q2242220) (← links)
• Additive stacking for disaggregate electricity demand forecasting (Q2245149) (← links)
• Machine learning approach for higher-order interactions detection to ecological communities management (Q2245975) (← links)
• Extending models via gradient boosting: an application to Mendelian models (Q2247455) (← links)
• Semiparametric estimation of a class of generalized linear models without smoothing (Q2252891) (← links)
• Variable selection and model choice in structured survival models (Q2255920) (← links)
• Detecting the impact area of BP deepwater horizon oil discharge: an analysis by time varying coefficient logistic models and boosted trees (Q2259710) (← links)
• Prediction and classification in nonlinear data analysis: something old, something new, something borrowed, something blue (Q2259888) (← links)
• Forward regression for Cox models with high-dimensional covariates (Q2274944) (← links)
• Oblique random survival forests (Q2281237) (← links)
• Predicting missing values: a comparative study on non-parametric approaches for imputation (Q2282599) (← links)
• Predictive analytics of insurance claims using multivariate decision trees (Q2283657) (← links)
• On the differences between \(L_2\) boosting and the Lasso (Q2288790) (← links)
• Inference for \(L_2\)-boosting (Q2302490) (← links)
• The \(\delta \)-machine: classification based on distances towards prototypes (Q2304085) (← links)
• Double machine learning with gradient boosting and its application to the Big \(N\) audit quality effect (Q2305992) (← links)
• Non-parametric learning of lifted restricted Boltzmann machines (Q2310287) (← links)
• Modeling binary time series using Gaussian processes with application to predicting sleep states (Q2317187) (← links)
• Nonlinear predictive models for multiple mediation analysis: with an application to explore ethnic disparities in anxiety and depression among cancer survivors (Q2318857) (← links)
• Sparse kernel deep stacking networks (Q2319474) (← links)
• Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier (Q2328398) (← links)
• Duality gap estimates for weak Chebyshev greedy algorithms in Banach spaces (Q2337146) (← links)
• Regression trees and forests for non-homogeneous Poisson processes (Q2339551) (← links)
• A Bayesian regression tree approach to identify the effect of nanoparticles' properties on toxicity profiles (Q2349582) (← links)
• Cross-conformal predictors (Q2352364) (← links)
• Significant vector learning to construct sparse kernel regression models (Q2383041) (← links)
• An integrated approach of data envelopment analysis and boosted generalized linear mixed models for efficiency assessment (Q2399309) (← links)
• Power comparison for propensity score methods (Q2418070) (← links)
• Accelerated gradient boosting (Q2425242) (← links)
• Early stopping in \(L_{2}\)Boosting (Q2445675) (← links)
• Boosting iterative stochastic ensemble method for nonlinear calibration of subsurface flow models (Q2449910) (← links)
• Neural network ensembles: evaluation of aggregation algorithms (Q2457679) (← links)
• An empirical study of using Rotation Forest to improve regressors (Q2470171) (← links)
• An efficient modified boosting method for solving classification problems (Q2479397) (← links)
• Constructing a speculative kernel machine for pattern classification (Q2488681) (← links)
• Boosting for high-dimensional linear models (Q2497175) (← links)
• Forecasting financial and macroeconomic variables using data reduction methods: new empirical evidence (Q2511793) (← links)
• A boosting method with asymmetric mislabeling probabilities which depend on covariates (Q2512782) (← links)
• An improved multiclass LogitBoost using adaptive-one-vs-one (Q2514757) (← links)
• Boosting with early stopping: convergence and consistency (Q2583412) (← links)
• Wavelet-based gradient boosting (Q2631350) (← links)
• A survey of deep network techniques all classifiers can adopt (Q2659274) (← links)
• Validating game-theoretic models of terrorism: insights from machine learning (Q2669076) (← links)
• Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis (Q2669442) (← links)