The following pages link to Bagging predictors (Q65108):
Displaying 50 items.
- Transfer learning for functional mean estimation: phase transition and adaptive algorithms (Q6550968) (← links)
- Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning (Q6557656) (← links)
- OCtS: an alternative of the t-Score method sensitive to outliers and correlation in feature selection (Q6558508) (← links)
- Different thresholding methods on Nearest Shrunken Centroid algorithm (Q6558512) (← links)
- Factor selection in screening experiments by aggregation over random models (Q6561273) (← links)
- Fusing multiple interval-valued fuzzy monotonic decision trees (Q6562312) (← links)
- Machine Learning vs. Survival Analysis Models: a study on right censored heart failure data (Q6562743) (← links)
- A fine-tuned estimator of a general convergence rate (Q6573724) (← links)
- Optimization in machine learning: a distribution-space approach (Q6575304) (← links)
- Cross-estimation for decision selection (Q6578156) (← links)
- Confidence intervals centred on bootstrap smoothed estimators: an impossibility result (Q6579387) (← links)
- Strong uniform laws of large numbers for bootstrap means and other randomly weighted sums (Q6580271) (← links)
- Automobile insurance claim occurrence prediction model based on ensemble learning (Q6580769) (← links)
- Trade-off between bagging and boosting for quantum separability-entanglement classification (Q6588838) (← links)
- Weight bound constraints in mean-variance models: a robust control theory foundation via machine learning (Q6592279) (← links)
- Stacking-based neural network for nonlinear time series analysis (Q6596733) (← links)
- The how and why of Bayesian nonparametric causal inference (Q6601995) (← links)
- Statistical computational learning (Q6602226) (← links)
- Use of majority votes in statistical learning (Q6604473) (← links)
- Recent advances in scaling-down sampling methods in machine learning (Q6607064) (← links)
- The use of machine learning techniques for assessing the potential of organizational resilience (Q6608506) (← links)
- Fluctuations, bias, variance and ensemble of learners: exact asymptotics for convex losses in high-dimension (Q6611433) (← links)
- Identification of representative trees in random forests based on a new tree-based distance measure (Q6613894) (← links)
- Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods (Q6617739) (← links)
- Hybrid modeling design patterns (Q6617841) (← links)
- Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses (Q6618445) (← links)
- Optimal ensemble construction for multistudy prediction with applications to mortality estimation (Q6618468) (← links)
- Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models (Q6623194) (← links)
- Variable selection in linear regression models: choosing the best subset is not always the best choice (Q6625369) (← links)
- Classification using ensemble learning under weighted misclassification loss (Q6625617) (← links)
- Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival (Q6625659) (← links)
- Reds: random ensemble deep spatial prediction (Q6626548) (← links)
- Variable selection for censored data using modified correlation adjusted correlation (MCAR) scores (Q6628007) (← links)
- Adaptively stacking ensembles for influenza forecasting (Q6628228) (← links)
- Pooling random forest and functional data analysis for biomedical signals supervised classification: theory and application to electrocardiogram data (Q6628360) (← links)
- Training support vector machines for dealing with the ImageNet challenging problem (Q6629044) (← links)
- DL 101: basic introduction to deep learning with its application in biomedical related fields (Q6629378) (← links)
- Sensitivity Prewarping for Local Surrogate Modeling (Q6631098) (← links)
- A Covariate-Regulated Sparse Subspace Learning Model and Its Application to Process Monitoring and Fault Isolation (Q6631131) (← links)
- Towards Improved Heliosphere Sky Map Estimation with Theseus (Q6631200) (← links)
- Network Estimation by Mixing: Adaptivity and More (Q6631716) (← links)
- Estimation of a predictor's importance by random forests when there is missing data: RISK prediction in liver surgery using laboratory data (Q6632702) (← links)
- Model averaging: a shrinkage perspective (Q6635565) (← links)
- Integrating graph structures into kernel regression models (Q6638678) (← links)
- Random logistic machine (RLM): Transforming statistical models into machine learning approach (Q6641279) (← links)
- Competence-conscious associative classification (Q6641620) (← links)
- Roughly balanced bagging for imbalanced data (Q6641624) (← links)
- Supervised learning via ensembles of diverse functional representations: the functional voting classifier (Q6643227) (← links)
- A survey of some recent developments in measures of association (Q6645569) (← links)
- Dynamic analysis and optimal control of leptospirosis based on Caputo fractional derivative (Q6647123) (← links)
