DRFLogitBoost: a double randomized decision forest incorporated with logitboosted decision stumps
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Combining diverse one-class classifiers
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Incorporation of a Regularization Term to Control Negative Correlation in Mixture of Experts
Neural Processing Letters
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
Traffic sign classifier adaption by semi-supervised co-training
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A parts-based multi-scale method for symbol recognition
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Exploiting label dependencies for improved sample complexity
Machine Learning
Ensemble learning for generalised eigenvalues proximal support vector machines
International Journal of Computer Applications in Technology
Smoothed emphasis for boosting ensembles
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air
International Journal of Knowledge Discovery in Bioinformatics
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
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Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.