The following pages link to Bagging predictors (Q65108):
Displaying 50 items.
- The combination of multiple classifiers using an evidential reasoning approach (Q2389681) (← links)
- Mixing partially linear regression models (Q2392079) (← links)
- Rough cognitive ensembles (Q2409100) (← links)
- PAC-Bayesian risk bounds for group-analysis sparse regression by exponential weighting (Q2418515) (← links)
- Hierarchical mixing linear support vector machines for nonlinear classification (Q2418742) (← links)
- Arbitrage of forecasting experts (Q2425238) (← links)
- First order random forests: Learning relational classifiers with complex aggregates (Q2433184) (← links)
- Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves (Q2433187) (← links)
- Training and assessing classification rules with imbalanced data (Q2435707) (← links)
- Optimal experimental design and some related control problems (Q2440603) (← links)
- Risk reduction for nonlinear prediction and its application to the surrogate data test (Q2448676) (← links)
- Combining the requirement information for software defect estimation in design time (Q2448855) (← links)
- Assessing the stability of classification trees using Florida birth data (Q2455414) (← links)
- Computer-intensive rate estimation, diverging statistics and scanning (Q2456022) (← links)
- Neural network ensembles: evaluation of aggregation algorithms (Q2457679) (← links)
- An empirical study of using Rotation Forest to improve regressors (Q2470171) (← links)
- Cross-validated bagged learning (Q2474238) (← links)
- The random electrode selection ensemble for EEG signal classification (Q2476577) (← links)
- Genetic algorithm-based feature set partitioning for classification problems (Q2476578) (← links)
- Building ensemble classifiers using belief functions and OWA operators (Q2476651) (← links)
- Maximum patterns in datasets (Q2478429) (← links)
- An efficient modified boosting method for solving classification problems (Q2479397) (← links)
- Model combination for credit risk assessment: a stacked generalization approach (Q2480229) (← links)
- Selected tree classifier combination based on both accuracy and error diversity (Q2485044) (← links)
- Algorithms for manipulation of level sets of nonparametric density estimates (Q2488405) (← links)
- Classifying G-protein coupled receptors with bagging classification tree (Q2490541) (← links)
- Additive regularization trade-off: fusion of training and validation levels in kernel methods (Q2491372) (← links)
- Automatic knowledge learning and case adaptation with a hybrid committee approach (Q2494724) (← links)
- Manufacturing lead time estimation using data mining (Q2496089) (← links)
- Diversification for better classification trees (Q2499153) (← links)
- Wavelet neural networks: a practical guide (Q2510734) (← 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)
- Learning a priori constrained weighted majority votes (Q2512901) (← links)
- Regression conformal prediction with random forests (Q2512902) (← links)
- On the fusion of threshold classifiers for categorization and dimensionality reduction (Q2513348) (← links)
- Classification of repeated measurements data using tree-based ensemble methods (Q2513351) (← links)
- Rejoinder on: Augmenting the bootstrap to analyze high dimensional genomic data (Q2517334) (← links)
- The method of ensembles of decision trees for the analysis of the electrical activity produced by the human brain (Q2519401) (← links)
- Complexities of convex combinations and bounding the generalization error in classification (Q2583410) (← links)
- A random forest guided tour (Q2629364) (← links)
- LiNearN: a new approach to nearest neighbour density estimator (Q2629871) (← links)
- VR-BFDT: a variance reduction based binary fuzzy decision tree induction method for protein function prediction (Q2630322) (← links)
- Impact of geometric uncertainty on hemodynamic simulations using machine learning (Q2631533) (← links)
- Prediction of FMN-binding residues with three-dimensional probability distributions of interacting atoms on protein surfaces (Q2632475) (← links)
- Ensemble learning HMM for motion recognition and retrieval by Isomap dimension reduction (Q2644508) (← links)
- Automatic color constancy algorithm selection and combination (Q2654234) (← links)
- A survey of deep network techniques all classifiers can adopt (Q2659274) (← links)
- Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification (Q2660967) (← links)
- A random forest based approach for predicting spreads in the primary catastrophe bond market (Q2665850) (← links)
