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ORCIDEnsembles in Machine Learning Applications 2011
- Oleg Okun
, Giorgio Valentini, Matteo Ré:
Ensembles in Machine Learning Applications. Studies in Computational Intelligence 373, Springer 2011, ISBN 978-3-642-22909-1 - Raymond S. Smith, Terry Windeatt:
Facial Action Unit Recognition Using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers. 1-20 - Miguel Ángel Bautista, Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva:
On the Design of Low Redundancy Error-Correcting Output Codes. 21-38 - Evgueni N. Smirnov, Matthijs Moed, Georgi I. Nalbantov, Ida G. Sprinkhuizen-Kuyper:
Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification. 39-58 - Cemre Zor, Terry Windeatt, Berrin A. Yanikoglu:
Bias-Variance Analysis of ECOC and Bagging Using Neural Nets. 59-73 - Benjamin Schowe, Katharina Morik:
Fast-Ensembles of Minimum Redundancy Feature Selection. 75-95 - Rakkrit Duangsoithong, Terry Windeatt:
Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers. 97-115 - Houtao Deng, Saylisse Dávila, George C. Runger, Eugene Tuv:
Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection. 117-131 - Stefano Ceccon, David Garway-Heath, David P. Crabb, Allan Tucker:
Ensembles of Bayesian Network Classifiers Using Glaucoma Data and Expertise. 133-150 - Alessandro Rozza, Gabriele Lombardi, Matteo Re, Elena Casiraghi, Giorgio Valentini, Paola Campadelli:
A Novel Ensemble Technique for Protein Subcellular Location Prediction. 151-167 - Haytham Elghazel, Alex Aussem, Florence Perraud:
Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests. 169-179 - Carlos Pardo, Juan J. Rodríguez Diez, José-Francisco Díez-Pastor, César Ignacio García-Osorio:
Random Oracles for Regression Ensembles. 181-199 - Pierluigi Casale, Oriol Pujol, Petia Radeva:
Embedding Random Projections in Regularized Gradient Boosting Machines. 201-216 - Giuliano Armano, Nima Hatami:
An Improved Mixture of Experts Model: Divide and Conquer Using Random Prototypes. 217-231 - Indre Zliobaite:
Three Data Partitioning Strategies for Building Local Classifiers. 233-250
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