Double committee adaboost
暂无分享,去创建一个
[1] Juan José Rodríguez Diez,et al. Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Ruy Luiz Milidiú,et al. Improving BAS committee performance with a semi-supervised approach , 2009, ESANN.
[3] Jun Kawai,et al. LOCATE: a mouse protein subcellular localization database , 2005, Nucleic Acids Res..
[4] Gonzalo Mart. Switching Class Labels to Generate Classication Ensembles , 2005 .
[5] Ludmila I. Kuncheva,et al. Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.
[6] Yoram Singer,et al. Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.
[7] Jian Guo,et al. TSSub: eukaryotic protein subcellular localization by extracting features from profiles , 2006, Bioinform..
[8] Chun-Xia Zhang,et al. RotBoost: A technique for combining Rotation Forest and AdaBoost , 2008, Pattern Recognit. Lett..
[9] Li Zhang,et al. Sparse ensembles using weighted combination methods based on linear programming , 2011, Pattern Recognit..
[10] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[11] Loris Nanni,et al. Ensemblator: An ensemble of classifiers for reliable classification of biological data , 2007, Pattern Recognit. Lett..
[12] Kagan Tumer,et al. Input decimated ensembles , 2003, Pattern Analysis & Applications.
[13] Loris Nanni,et al. Reduced Reward-punishment editing for building ensembles of classifiers , 2011, Expert Syst. Appl..
[14] Lior Shamir,et al. Source Code for Biology and Medicine Open Access Wndchrm – an Open Source Utility for Biological Image Analysis , 2022 .
[15] Nojun Kwak,et al. Feature extraction for classification problems and its application to face recognition , 2008, Pattern Recognit..
[16] Yoav Freund,et al. An Adaptive Version of the Boost by Majority Algorithm , 1999, COLT.
[17] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[18] Yang Kai,et al. Genetic Algorithm Based Optimization for AdaBoost , 2008, 2008 International Conference on Computer Science and Software Engineering.
[19] Osamu Watanabe,et al. Scaling Up a Boosting-Based Learner via Adaptive Sampling , 2000, PAKDD.
[20] Anthony J. Bonner,et al. Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements , 2007, BMC Bioinformatics.
[21] Rocco A. Servedio,et al. Smooth boosting and learning with malicious noise , 2003 .
[22] Vasile Palade,et al. A neural network based multi-classifier system for gene identification in DNA sequences , 2004, Neural Computing & Applications.
[23] Guido Bologna,et al. A comparison study on protein fold recognition , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..
[24] De-Shuang Huang,et al. Cancer classification using Rotation Forest , 2008, Comput. Biol. Medicine.
[25] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[26] David H. Wolpert,et al. Coevolutionary free lunches , 2005, IEEE Transactions on Evolutionary Computation.
[27] Aníbal R. Figueiras-Vidal,et al. Committees of Adaboost ensembles with modified emphasis functions , 2010, Neurocomputing.
[28] Tom Bylander,et al. Using Validation Sets to Avoid Overfitting in AdaBoost , 2006, FLAIRS.
[29] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[30] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[31] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[32] Gonzalo Martínez-Muñoz,et al. Switching class labels to generate classification ensembles , 2005, Pattern Recognit..
[33] Robert Sabourin,et al. From dynamic classifier selection to dynamic ensemble selection , 2008, Pattern Recognit..
[34] H. Altay Güvenir,et al. Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals , 1998, Artif. Intell. Medicine.
[35] Kuniaki Uehara,et al. Improvement of Boosting Algorithm by Modifying the Weighting Rule , 2004, Annals of Mathematics and Artificial Intelligence.
[36] Robert F. Murphy,et al. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells , 2001, Bioinform..
[37] Jiri Matas,et al. On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[38] Loris Nanni,et al. Ensemble generation and feature selection for the identification of students with learning disabilities , 2009, Expert Syst. Appl..
[39] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[40] Gunnar Rätsch,et al. Soft Margins for AdaBoost , 2001, Machine Learning.