The following pages link to Bagging predictors (Q65108):
Displaying 50 items.
- Supervised classification of curves via a combined use of functional data analysis and tree-based methods (Q6104427) (← links)
- Bootstrapping some GLM and survival regression variable selection estimators (Q6106216) (← links)
- Naive automated machine learning (Q6106457) (← links)
- Algorithm selection on a meta level (Q6106469) (← links)
- Random Forests for Spatially Dependent Data (Q6107238) (← links)
- Fault classification via energy based features of two-dimensional image data (Q6107562) (← links)
- A Neural Approach to Improve the Lee-Carter Mortality Density Forecasts (Q6107672) (← links)
- Efficient permutation testing of variable importance measures by the example of random forests (Q6113742) (← links)
- Use of random forest for assessing the effect of water quality parameters on the biological status of surface waters (Q6113805) (← links)
- Active learning by query by committee with robust divergences (Q6115105) (← links)
- Subsampling based variable selection for generalized linear models (Q6115531) (← links)
- Model averaging for support vector classifier by cross-validation (Q6117027) (← links)
- Space-dependent turbulence model aggregation using machine learning (Q6119255) (← links)
- An ensemble EM algorithm for Bayesian variable selection (Q6121783) (← links)
- Reproducible model selection using bagged posteriors (Q6122016) (← links)
- Two efficient selection methods for high‐dimensional <scp>CD‐CAT</scp> utilizing max‐marginals factor from <scp>MAP</scp> query and ensemble learning approach (Q6127086) (← links)
- Geometry of EM and related iterative algorithms (Q6138788) (← links)
- Mixture of inhomogeneous matrix models for species‐rich ecosystems (Q6139123) (← links)
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- Exploratory machine learning with unknown unknowns (Q6152674) (← links)
- Modelplasticity and abductive decision making (Q6156165) (← links)
- Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification (Q6158540) (← links)
- A comparison of two dissimilarity functions for mixed-type predictor variables in the \(\delta\)-machine (Q6161660) (← links)
- Dynamic risk prediction triggered by intermediate events using survival tree ensembles (Q6161880) (← links)
- Estimation and inference of treatment effects with \(L_2\)-boosting in high-dimensional settings (Q6163259) (← links)
- Model averaging prediction by \(K\)-fold cross-validation (Q6163281) (← links)
- 2-step gradient boosting approach to selectivity bias correction in tax audit: an application to the VAT gap in Italy (Q6163506) (← links)
- Derandomizing Knockoffs (Q6165283) (← links)
- Linear and nonlinear model predictive control (MPC) for regulating pedestrian flows with discrete speed instructions (Q6167701) (← links)
- Predicting the spatial distribution of stable isotopes in precipitation using a machine learning approach: a comparative assessment of random forest variants (Q6168893) (← links)
- Neighborhood-based cross fitting approach to treatment effects with high-dimensional data (Q6170545) (← links)
- Data-driven decision model based on local two-stage weighted ensemble learning (Q6170946) (← links)
- Ensemble learning for the partial label ranking problem (Q6181961) (← links)
- Solute transport prediction in heterogeneous porous media using random walks and machine learning (Q6191737) (← links)
- Back-propagating errors through artificial neural networks for variable selection (Q6201206) (← links)
- Selective Pseudo-Label Clustering (Q6488141) (← links)
- Semi-supervised classifier ensemble model for high-dimensional data (Q6490345) (← links)
- Model averaged tail area confidence intervals in nested linear regression models (Q6491771) (← links)
- A supervised fuzzy measure learning algorithm for combining classifiers (Q6492551) (← links)
- Explainable ensemble trees (Q6538404) (← links)
- Learning from high dimensional data based on weighted feature importance in decision tree ensembles (Q6538421) (← links)
- Distributions and bootstrap for data-based stochastic programming (Q6538818) (← links)
- On confidence intervals centred on bootstrap smoothed estimators (Q6541507) (← links)
- A bootstrap-augmented alternating expectation-conditional maximization algorithm for mixtures of factor analyzers (Q6541519) (← links)
- Testing prediction algorithms as null hypotheses: application to assessing the performance of deep neural networks (Q6541555) (← links)
- Causal effect random forest of interaction trees for learning individualized treatment regimes with multiple treatments in observational studies (Q6543882) (← links)
- Machine collaboration (Q6548943) (← links)
- A review on design inspired subsampling for big data (Q6549149) (← links)
