← Core Methods
Machine Learning › Core Methods ›

Ensemble Methods

233 directly classified papers

Papers per year

Papers

Ranking Forests JMLR 2013
Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training ICML 2013
Collaborative Active Learning of a Kernel Machine Ensemble for Recognition ICCV 2013
Structured Forests for Fast Edge Detection ICCV 2013
Alternating Regression Forests for Object Detection and Pose Estimation ICCV 2013
Understanding variable importances in forests of randomized trees NIPS 2013
Direct 0-1 Loss Minimization and Margin Maximization with Boosting NIPS 2013
Decision Jungles: Compact and Rich Models for Classification NIPS 2013
Reservoir Boosting : Between Online and Offline Ensemble Learning NIPS 2013
Alternating Decision Forests CVPR 2013
Saliency Aggregation: A Data-Driven Approach CVPR 2013
Semi-supervised Node Splitting for Random Forest Construction CVPR 2013
Multi-label Subspace Ensemble AISTATS 2012
Bayesian Classifier Combination AISTATS 2012
Multiplicative Forests for Continuous-Time Processes NIPS 2012
Context-Sensitive Decision Forests for Object Detection NIPS 2012
Variance Penalizing AdaBoost NIPS 2011
Active Classification based on Value of Classifier NIPS 2011
(RF)^2 -- Random Forest Random Field NIPS 2010
Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking ACML 2010
Exploiting the High Predictive Power of Multi-class Subgroups ACML 2010
Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models NIPS 2009
Adaptive Martingale Boosting NIPS 2008
McRank: Learning to Rank Using Multiple Classification and Gradient Boosting NIPS 2007
Boosting Algorithms for Maximizing the Soft Margin NIPS 2007