The following pages link to Bagging predictors (Q65108):
Displaying 50 items.
- Wrappers for feature subset selection (Q1127360) (← links)
- An efficient method to estimate bagging's generalization error (Q1289083) (← links)
- symbolicDA (Q1351609) (← links)
- Heuristics of instability and stabilization in model selection (Q1354430) (← links)
- Attribute bagging: Improving accuracy of classifier ensembles by using random feature subsets. (Q1402701) (← links)
- Double-bagging: Combining classifiers by bootstrap aggregation (Q1402702) (← links)
- Vote counting measures for ensemble classifiers. (Q1425964) (← links)
- Constructing support vector machine ensemble. (Q1425966) (← links)
- Bayesian model averaging: A tutorial. (with comments and a rejoinder). (Q1431179) (← links)
- A concrete statistical realization of Kleinberg's stochastic dicrimination for pattern recognition. I: Two-class classification (Q1431432) (← links)
- Noisy replication in skewed binary classification. (Q1583065) (← links)
- Randomizing outputs to increase prediction accuracy (Q1584833) (← links)
- Multicriteria classification and sorting methods: A literature review (Q1600903) (← links)
- A hierarchical classification strategy for digital documents (Q1601600) (← links)
- Efficient data reconciliation (Q1602501) (← links)
- A geometric approach to leveraging weak learners (Q1603593) (← links)
- Ensembling neural networks: Many could be better than all (Q1605287) (← links)
- Performance evaluation of bagging and boosting in nonparametric regression. (Q1605882) (← links)
- Interpreting neural-network results: a simulation study. (Q1606480) (← links)
- Estimator selection and combination in scalar-on-function regression (Q1615246) (← links)
- On principal components regression, random projections, and column subsampling (Q1616329) (← links)
- An ensemble approach for in silico prediction of Ames mutagenicity (Q1617490) (← links)
- Random average shifted histograms (Q1623662) (← links)
- Accurate ensemble pruning with PL-bagging (Q1623764) (← links)
- Ensemble of a subset of \(k\)NN classifiers (Q1630834) (← links)
- Stable feature selection for biomarker discovery (Q1631176) (← links)
- Ensembles for multi-target regression with random output selections (Q1631826) (← links)
- An approximate likelihood perspective on ABC methods (Q1636827) (← links)
- Distant diversity in dynamic class prediction (Q1639212) (← links)
- Evaluating the importance of different communication types in romantic tie prediction on social media (Q1639257) (← links)
- Subsampling based inference for \(U\) statistics under thick tails using self-normalization (Q1642255) (← links)
- Gradient boosting for high-dimensional prediction of rare events (Q1658126) (← links)
- Should we impute or should we weight? Examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables (Q1658370) (← links)
- Deriving optimal data-analytic regimes from benchmarking studies (Q1658481) (← links)
- Using the Bayesian Shtarkov solution for predictions (Q1658740) (← links)
- A triplot for multiclass classification visualisation (Q1660130) (← links)
- Noise peeling methods to improve boosting algorithms (Q1660240) (← links)
- Addressing overfitting and underfitting in Gaussian model-based clustering (Q1663118) (← links)
- Grouped variable importance with random forests and application to multiple functional data analysis (Q1663198) (← links)
- A robust AdaBoost.RT based ensemble extreme learning machine (Q1665122) (← links)
- A new ensemble method with feature space partitioning for high-dimensional data classification (Q1666085) (← links)
- Random forest with adaptive local template for pedestrian detection (Q1666540) (← links)
- Optimizing area under the ROC curve using semi-supervised learning (Q1677038) (← links)
- Dynamic classifier aggregation using interaction-sensitive fuzzy measures (Q1677625) (← links)
- Weighted classifier ensemble based on quadratic form (Q1677847) (← links)
- Accelerating difficulty estimation for conformal regression forests (Q1680848) (← links)
- Multi-target regression via input space expansion: treating targets as inputs (Q1689552) (← links)
- Context-based unsupervised ensemble learning and feature ranking (Q1689607) (← links)
- A new robust classifier on noise domains: bagging of Credal C4.5 trees (Q1694254) (← links)
- Modeling threshold interaction effects through the logistic classification trunk (Q1695093) (← links)
