The following pages link to Bagging predictors (Q65108):
Displaying 50 items.
- Recent advances in statistical methodologies in evaluating program for high-dimensional data (Q2132738) (← links)
- An eager splitting strategy for online decision trees in ensembles (Q2134037) (← links)
- Partitioning around medoids clustering and random forest classification for GIS-informed imputation of fluoride concentration data (Q2135383) (← links)
- Rates of convergence for random forests via generalized U-statistics (Q2136608) (← links)
- Modelling and forecasting based on recursive incomplete pseudoinverse matrices (Q2139889) (← links)
- Explainable models of credit losses (Q2140185) (← links)
- Why estimation alone causes Markowitz portfolio selection to fail and what we might do about it (Q2140218) (← links)
- A random forest algorithm to improve the Lee-Carter mortality forecasting: impact on q-forward (Q2153637) (← links)
- ESG score prediction through random forest algorithm (Q2155224) (← links)
- Dynamic ensemble selection based on hesitant fuzzy multiple criteria decision making (Q2156498) (← links)
- Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic fuzzy-rough feature selection (Q2157067) (← links)
- Self-triggered control of probabilistic Boolean control networks: a reinforcement learning approach (Q2159969) (← links)
- Ordinal trees and random forests: score-free recursive partitioning and improved ensembles (Q2169870) (← links)
- Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles (Q2178143) (← links)
- Banzhaf random forests: cooperative game theory based random forests with consistency (Q2182870) (← links)
- Cost-sensitive business failure prediction when misclassification costs are uncertain: a heterogeneous ensemble selection approach (Q2183867) (← links)
- Rejoinder on: ``Hierarchical inference for genome-wide association studies: a view on methodology with software'' (Q2184393) (← links)
- Cascade interpolation learning with double subspaces and confidence disturbance for imbalanced problems (Q2185618) (← links)
- Multi-output parameter-insensitive kernel twin SVR model (Q2185673) (← links)
- A robust outlier control framework for classification designed with family of homotopy loss function (Q2188214) (← links)
- Micromechanics-based surrogate models for the response of composites: a critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks (Q2190108) (← links)
- Models as approximations. II. A model-free theory of parametric regression (Q2194567) (← links)
- Induction of classification rules by Gini-index based rule generation (Q2195438) (← links)
- Propositionalization and embeddings: two sides of the same coin (Q2203327) (← links)
- Double random forest (Q2203331) (← links)
- Robust classification via MOM minimization (Q2203337) (← links)
- A Bayesian perspective of statistical machine learning for big data (Q2203387) (← links)
- Random forest with acceptance-rejection trees (Q2203396) (← links)
- Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images (Q2208386) (← links)
- Modeling of time series using random forests: theoretical developments (Q2209824) (← links)
- A model-free Bayesian classifier (Q2212071) (← links)
- A weighted multiple classifier framework based on random projection (Q2214960) (← links)
- Fast greedy \(\mathcal{C} \)-bound minimization with guarantees (Q2217455) (← links)
- A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates (Q2218385) (← links)
- SIRUS: stable and interpretable RUle set for classification (Q2219234) (← links)
- C443: a methodology to see a forest for the trees (Q2220704) (← links)
- A look at robustness and stability of \(\ell_1\)-versus \(\ell_0\)-regularization: discussion of papers by Bertsimas et al. and Hastie et al. (Q2225318) (← links)
- Enhancing techniques for learning decision trees from imbalanced data (Q2228292) (← links)
- Reduced rank regression with matrix projections for high-dimensional multivariate linear regression model (Q2233570) (← links)
- Fusion of geochemical and remote-sensing data for lithological mapping using random forest metric learning (Q2238097) (← links)
- Forecasting bankruptcy using biclustering and neural network-based ensembles (Q2241078) (← links)
- Two-level regression method using ensembles of trees with optimal divergence (Q2246886) (← links)
- A deep learning semiparametric regression for adjusting complex confounding structures (Q2247451) (← links)
- Extending models via gradient boosting: an application to Mendelian models (Q2247455) (← links)
- Forecast aggregation via recalibration (Q2251477) (← links)
- Ensemble Gaussian mixture models for probability density estimation (Q2255769) (← links)
- Studying the bandwidth in \(k\)-sample smooth tests (Q2255860) (← links)
- A combination selection algorithm on forecasting (Q2256180) (← links)
- Estimating the functional form of a continuous covariate's effect on survival time (Q2257607) (← links)
- GA-Ensemble: a genetic algorithm for robust ensembles (Q2259225) (← links)
