The following pages link to Bagging predictors (Q65108):
Displaying 50 items.
- Boosting and instability for regression trees (Q959181) (← links)
- Maximum a posteriori pruning on decision trees and its application to bootstrap bumping (Q959196) (← links)
- Responder identification in clinical trials with censored data (Q959231) (← links)
- Stock and bond return predictability: the discrimination power of model selection criteria (Q959244) (← links)
- Bootstrap estimated true and false positive rates and ROC curve (Q961178) (← links)
- Standard errors for bagged and random forest estimators (Q961193) (← links)
- A similarity measure to assess the stability of classification trees (Q961261) (← links)
- Using boosting to prune double-bagging ensembles (Q961263) (← links)
- Survival prediction using gene expression data: a review and comparison (Q961312) (← links)
- Multiclass classification and gene selection with a stochastic algorithm (Q961825) (← links)
- Multivariate trees for mixed outcomes (Q961861) (← links)
- Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography (Q961895) (← links)
- Bagging constraint score for feature selection with pairwise constraints (Q962802) (← links)
- Ore grade prediction using a genetic algorithm and clustering based ensemble neural network model (Q964855) (← links)
- Disparate data fusion for protein phosphorylation prediction (Q970186) (← links)
- Iterative Boolean combination of classifiers in the ROC space: an application to anomaly detection with HMMs (Q975181) (← links)
- Spoofing protection for fingerprint scanner by fusing ridge signal and valley noise (Q975196) (← links)
- Navigating random forests and related advances in algorithmic modeling (Q975577) (← links)
- Consistency of random survival forests (Q979192) (← links)
- Learn\(^{++}\).MF: A random subspace approach for the missing feature problem (Q991269) (← links)
- Information theoretic combination of pattern classifiers (Q991948) (← links)
- An asymmetric classifier based on partial least squares (Q991957) (← links)
- Cost-sensitive boosting for classification of imbalanced data (Q996413) (← links)
- Functional dissipation microarrays for classification (Q996417) (← links)
- Decision trees using model ensemble-based nodes (Q996440) (← links)
- EROS: Ensemble rough subspaces (Q996469) (← links)
- Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation (Q997040) (← links)
- Feature selection via sensitivity analysis of SVM probabilistic outputs (Q1009227) (← links)
- Tree-structured model diagnostics for linear regression (Q1009312) (← links)
- Exact bootstrap \(k\)-nearest neighbor learners (Q1009331) (← links)
- Novel approaches to probabilistic neural networks through bagging and evolutionary estimating of prior probabilities (Q1009347) (← links)
- Diversity of ability and cognitive style for group decision processes (Q1010126) (← links)
- Experience-consistent modeling: regression and classification problems (Q1012866) (← links)
- Data preparation using data quality matrices for classification mining (Q1014996) (← links)
- Parallelizing AdaBoost by weights dynamics (Q1019879) (← links)
- Classifying densities using functional regression trees: applications in oceanology (Q1020163) (← links)
- Classification by ensembles from random partitions of high-dimensional data (Q1020719) (← links)
- Robust variable selection using least angle regression and elemental set sampling (Q1020812) (← links)
- Trimmed bagging (Q1020824) (← links)
- Classification tree analysis using TARGET (Q1023462) (← links)
- A local boosting algorithm for solving classification problems (Q1023522) (← links)
- Empirical characterization of random forest variable importance measures (Q1023556) (← links)
- On properties of predictors derived with a two-step bootstrap model averaging approach -- a simulation study in the linear regression model (Q1023609) (← links)
- Comparisons of titer estimation methods for multiplexed pneumococcal opsonophagocytic Killing assay (Q1023868) (← links)
- Gaussian processes and limiting linear models (Q1023934) (← links)
- Negative correlation in incremental learning (Q1024030) (← links)
- Methods and algorithms of collective recognition (Q1027647) (← links)
- Using economic and financial information for stock selection (Q1031950) (← links)
- Bayesian bootstrap prediction (Q1036704) (← links)
- Forecasting cancellation rates for services booking revenue management using data mining (Q1039810) (← links)
