The following pages link to Bagging predictors (Q65108):
Displaying 50 items.
- Analysis of web visit histories. II: Predicting navigation by nested STUMP regression trees (Q1695100) (← links)
- Robust regression using biased objectives (Q1698865) (← links)
- Bootstrap bias corrections for ensemble methods (Q1702284) (← links)
- Bayesian additive regression trees using Bayesian model averaging (Q1704023) (← links)
- The online performance estimation framework: heterogeneous ensemble learning for data streams (Q1707471) (← links)
- Wide consensus aggregation in the Wasserstein space. Application to location-scatter families (Q1708997) (← links)
- Boosting imbalanced data learning with Wiener process oversampling (Q1712569) (← links)
- Comparison of machine learning methods for copper ore grade estimation (Q1715372) (← links)
- An incremental classification algorithm for mining data with feature space heterogeneity (Q1718147) (← links)
- Adaptive linear and normalized combination of radial basis function networks for function approximation and regression (Q1719381) (← links)
- Shape constraints in economics and operations research (Q1730901) (← links)
- Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior (Q1734841) (← links)
- Fitting random cash management models to data (Q1734858) (← links)
- Credit spread approximation and improvement using random forest regression (Q1735198) (← links)
- Algorithms for drug sensitivity prediction (Q1736854) (← links)
- Forecasting using random subspace methods (Q1740303) (← links)
- Generalized random shapelet forests (Q1741268) (← links)
- Cluster ensembles: a survey of approaches with recent extensions and applications (Q1750312) (← links)
- A nearest neighbour extension to project duration forecasting with artificial intelligence (Q1751933) (← links)
- Exploring the sources of uncertainty: why does bagging for time series forecasting work? (Q1754348) (← links)
- Improving bagging performance through multi-algorithm ensembles (Q1762211) (← links)
- Classification of multivariate time series and structured data using constructive induction (Q1774584) (← links)
- Cost-sensitive learning and decision making revisited (Q1779549) (← links)
- Nonparametric bootstrap prediction (Q1781189) (← links)
- Correcting classifiers for sample selection bias in two-phase case-control studies (Q1784139) (← links)
- High-dimensional inference: confidence intervals, \(p\)-values and R-software \texttt{hdi} (Q1790302) (← links)
- A novel margin-based measure for directed hill climbing ensemble pruning (Q1793078) (← links)
- Stable graphical model estimation with random forests for discrete, continuous, and mixed variables (Q1800084) (← links)
- Arcing classifiers. (With discussion) (Q1807115) (← links)
- Boosting the margin: a new explanation for the effectiveness of voting methods (Q1807156) (← links)
- Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors) (Q1848780) (← links)
- On weak base hypotheses and their implications for boosting regression and classification (Q1848929) (← links)
- Analyzing bagging (Q1848962) (← links)
- Discriminant feature extraction using empirical probability density estimation and a local basis library (Q1856710) (← links)
- Partitioning algorithms and combined model integration for data mining (Q1861614) (← links)
- Combining statistical and reinforcement learning in rule-based classification (Q1861616) (← links)
- Bounding the generalization error of convex combinations of classifiers: Balancing the dimensionality and the margins. (Q1872344) (← links)
- Generalization error of combined classifiers. (Q1872713) (← links)
- Least angle regression. (With discussion) (Q1879940) (← links)
- Generalization bounds for averaged classifiers (Q1879971) (← links)
- Is it worth generating rules from neural network ensembles? (Q1884273) (← links)
- Population theory for boosting ensembles. (Q1884600) (← links)
- On the Bayes-risk consistency of regularized boosting methods. (Q1884602) (← links)
- Optimal aggregation of classifiers in statistical learning. (Q1884608) (← links)
- Learning hybrid neuro-fuzzy classifier models from data: to combine or not to combine? (Q1885675) (← links)
- Stability and scalability in decision trees (Q1887226) (← links)
- Sensory analysis in the food industry as a tool for marketing decisions (Q1928204) (← links)
- Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences (Q1928210) (← links)
- Boosting random subspace method (Q1932108) (← links)
- Exploiting unlabeled data to enhance ensemble diversity (Q1944971) (← links)
