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Ensemble Learning

347 directly classified papers

Papers per year

Papers

Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising IJCNLP 2017
SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification SEMEVAL 2017
Confident Multiple Choice Learning ICML 2017
Multi-Target Ensemble Learning for Monaural Speech Separation INTERSPEECH 2017
Learning and Applying Case Adaptation Rules for Classification: An Ensemble Approach IJCAI 2017
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests JMLR 2016
Sequence Student-Teacher Training of Deep Neural Networks INTERSPEECH 2016
Swapout: Learning an ensemble of deep architectures NIPS 2016
mlr: Machine Learning in R JMLR 2016
A Randomized Ensemble Approach to Industrial CT Segmentation ICCV 2015
DART: Dropouts meet Multiple Additive Regression Trees AISTATS 2015
Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables ICCV 2015
Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting ICML 2014
Theoretical Analyses on Ensemble and Multiple Kernel Regressors ACML 2014
Multi-Resolution Cascades for Multiclass Object Detection NIPS 2014
Learning with Pseudo-Ensembles NIPS 2014
Consistency of weighted majority votes NIPS 2014
A Drifting-Games Analysis for Online Learning and Applications to Boosting NIPS 2014
Mondrian Forests: Efficient Online Random Forests NIPS 2014
Efficient Boosted Exemplar-based Face Detection CVPR 2014
Open Problem: A (Missing) Boosting-type Convergence Result for AdaBoost.MH with Factorized Multi-class Classifiers COLT 2014
Multi-Class Deep Boosting NIPS 2014
Diverse Randomized Agents Vote to Win NIPS 2014
Deep Boosting ICML 2014
A Convergence Rate Analysis for LogitBoost, MART and Their Variant ICML 2014