The following pages link to Bagging predictors (Q65108):
Displaying 50 items.
- A review of survival trees (Q1950331) (← links)
- Applying randomness effectively based on random forests for classification task of datasets of insufficient information (Q1952805) (← links)
- Statistical uncertainty estimation using random forests and its application to drought forecast (Q1955344) (← links)
- Multiple-view multiple-learner active learning (Q1957871) (← links)
- Regularization in skewed binary classification (Q1966373) (← links)
- From data to stochastic models (Q1976343) (← links)
- Meta-analysis of voice disorders databases and applied machine learning techniques (Q1979643) (← links)
- Machine learning based classification of normal, slow and fast walking by extracting multimodal features from stride interval time series (Q1980091) (← links)
- A comparative study of the leading machine learning techniques and two new optimization algorithms (Q1991232) (← links)
- An instance-based learning recommendation algorithm of imbalance handling methods (Q2010579) (← links)
- The reliability of classification of terminal nodes in GUIDE decision tree to predict the nonalcoholic fatty liver disease (Q2013940) (← links)
- Determining cutoff point of ensemble trees based on sample size in predicting clinical dose with DNA microarray data (Q2013966) (← links)
- Rotation Forests for regression (Q2016344) (← links)
- Forecasting corporate failure using ensemble of self-organizing neural networks (Q2028771) (← links)
- Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change (Q2029637) (← links)
- Widening: using parallel resources to improve model quality (Q2036761) (← links)
- Batch mode active learning framework and its application on valuing large variable annuity portfolios (Q2038226) (← links)
- A selective overview of deep learning (Q2038303) (← links)
- A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning (Q2044333) (← links)
- Design-unbiased statistical learning in survey sampling (Q2051018) (← links)
- Ensemble learning based on approximate reducts and bootstrap sampling (Q2056336) (← links)
- SVM-boosting based on Markov resampling: theory and algorithm (Q2057733) (← links)
- Adaboost-based ensemble of polynomial chaos expansion with adaptive sampling (Q2060149) (← links)
- Machine learning for credit scoring: improving logistic regression with non-linear decision-tree effects (Q2060438) (← links)
- Fundamental ratios as predictors of ESG scores: a machine learning approach (Q2064635) (← links)
- Sampling from non-smooth distributions through Langevin diffusion (Q2065460) (← links)
- Bootstrapping multiple linear regression after variable selection (Q2066517) (← links)
- An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests (Q2066746) (← links)
- Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches (Q2070704) (← links)
- Deep learning and multivariate time series for cheat detection in video games (Q2071484) (← links)
- Uncertainty quantification for honest regression trees (Q2072416) (← links)
- Optimal selection of sample-size dependent common subsets of covariates for multi-task regression prediction (Q2074281) (← links)
- Bagging-enhanced sampling schedule for functional quadratic regression (Q2074638) (← links)
- Hierarchical resampling for bagging in multistudy prediction with applications to human neurochemical sensing (Q2080731) (← links)
- Computational efficiency of bagging bootstrap bandwidth selection for density estimation with big data (Q2087094) (← links)
- Random clustering forest for extended belief rule-based system (Q2099900) (← links)
- High-dimensional correlation matrix estimation for general continuous data with Bagging technique (Q2102349) (← links)
- Explainable online ensemble of deep neural network pruning for time series forecasting (Q2102399) (← links)
- An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm (Q2103281) (← links)
- Operational research and artificial intelligence methods in banking (Q2106712) (← links)
- Random forest estimation of conditional distribution functions and conditional quantiles (Q2106811) (← links)
- Robust modeling of unknown dynamical systems via ensemble averaged learning (Q2112552) (← links)
- Asymptotic properties of high-dimensional random forests (Q2112821) (← links)
- Ensemble learning from model based trees with application to differential price sensitivity assessment (Q2127069) (← links)
- A review on instance ranking problems in statistical learning (Q2127240) (← links)
- Optimal Bayesian design for model discrimination via classification (Q2128059) (← links)
- A comparative study of machine learning models for predicting the state of reactive mixing (Q2128488) (← links)
- Comparing boosting and bagging for decision trees of rankings (Q2129304) (← links)
- Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor (Q2130290) (← links)
- Generalized bagging (Q2132055) (← links)
