Oblique Decision Tree Ensemble via Multisurface Proximal Support Vector Machine
暂无分享,去创建一个
[1] O. Mangasarian,et al. NONLINEAR PERTURBATION OF LINEAR PROGRAMS , 1979 .
[2] Tomaso Poggio,et al. Probabilistic Solution of Ill-Posed Problems in Computational Vision , 1987 .
[3] Gérard Dreyfus,et al. Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.
[4] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[5] Isabelle Guyon,et al. Comparison of classifier methods: a case study in handwritten digit recognition , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).
[6] Thomas G. Dietterich,et al. Error-Correcting Output Coding Corrects Bias and Variance , 1995, ICML.
[7] R. E. Lee,et al. Distribution-free multiple comparisons between successive treatments , 1995 .
[8] Thomas G. Dietterich,et al. Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..
[9] Steven L. Salzberg,et al. On growing better decision trees from data , 1996 .
[10] Leo Breiman,et al. Bias, Variance , And Arcing Classifiers , 1996 .
[11] Ron Kohavi,et al. Bias Plus Variance Decomposition for Zero-One Loss Functions , 1996, ICML.
[12] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[13] Nello Cristianini,et al. Large Margin DAGs for Multiclass Classification , 1999, NIPS.
[14] Thomas G. Dietterich. Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.
[15] Ja-Chen Lin,et al. A new LDA-based face recognition system which can solve the small sample size problem , 1998, Pattern Recognit..
[16] Tomaso A. Poggio,et al. Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..
[17] James S. Goerss,et al. Tropical Cyclone Track Forecasts Using an Ensemble of Dynamical Models , 2000 .
[18] Chandrika Kamath,et al. Inducing oblique decision trees with evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..
[19] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[20] Jerome H. Friedman,et al. On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality , 2004, Data Mining and Knowledge Discovery.
[21] Gareth James,et al. Variance and Bias for General Loss Functions , 2003, Machine Learning.
[22] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[23] Jun Chen,et al. Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes , 2004, BMC Bioinformatics.
[24] Yuxiao Hu,et al. Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Kurt Hornik,et al. The Design and Analysis of Benchmark Experiments , 2005 .
[26] Lior Rokach,et al. Top-down induction of decision trees classifiers - a survey , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[27] Witold Pedrycz,et al. Genetically optimized fuzzy decision trees , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[28] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[29] Yi Lin,et al. Random Forests and Adaptive Nearest Neighbors , 2006 .
[30] Jiawei Han,et al. Orthogonal Laplacianfaces for Face Recognition , 2006, IEEE Transactions on Image Processing.
[31] Juan José Rodríguez Diez,et al. Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[33] Olvi L. Mangasarian,et al. Multisurface proximal support vector machine classification via generalized eigenvalues , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] Bjoern H Menze,et al. Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy , 2007, Analytical and bioanalytical chemistry.
[35] Peter Kokol,et al. Effectiveness of Rotation Forest in Meta-learning Based Gene Expression Classification , 2007, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07).
[36] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[37] Chong Jin Ong,et al. A Feature Selection Method for Multilevel Mental Fatigue EEG Classification , 2007, IEEE Transactions on Biomedical Engineering.
[38] Juan José Rodríguez Diez,et al. Classifier Ensembles with a Random Linear Oracle , 2007, IEEE Transactions on Knowledge and Data Engineering.
[39] Lawrence O. Hall,et al. A Comparison of Decision Tree Ensemble Creation Techniques , 2007 .
[40] Li Zhang,et al. Decision Tree Support Vector Machine , 2007, Int. J. Artif. Intell. Tools.
[41] Xudong Jiang,et al. Eigenfeature Regularization and Extraction in Face Recognition , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] Marco Wiering,et al. Ensemble Algorithms in Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[43] Chun-Xia Zhang,et al. RotBoost: A technique for combining Rotation Forest and AdaBoost , 2008, Pattern Recognit. Lett..
[44] Xizhao Wang,et al. Induction of multiple fuzzy decision trees based on rough set technique , 2008, Inf. Sci..
[45] De-Shuang Huang,et al. Cancer classification using Rotation Forest , 2008, Comput. Biol. Medicine.
[46] C. Tappert,et al. A Genetic Algorithm for Constructing Compact Binary Decision Trees , 2009 .
[47] Bjoern H. Menze,et al. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data , 2009, BMC Bioinformatics.
[48] Xi-Zhao Wang,et al. Improving Generalization of Fuzzy IF--THEN Rules by Maximizing Fuzzy Entropy , 2009, IEEE Transactions on Fuzzy Systems.
[49] Thomas Martinetz,et al. BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection , 2011, BMC Bioinformatics.
[50] Kyungsook Han,et al. Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor. , 2010, Protein and peptide letters.
[51] Xudong Jiang,et al. Linear Subspace Learning-Based Dimensionality Reduction , 2011, IEEE Signal Processing Magazine.
[52] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[53] Gerrit K. Janssens,et al. Pareto-optimality of oblique decision trees from evolutionary algorithms , 2011, J. Glob. Optim..
[54] Akin Özçift,et al. SVM Feature Selection Based Rotation Forest Ensemble Classifiers to Improve Computer-Aided Diagnosis of Parkinson Disease , 2011, Journal of Medical Systems.
[55] P. Suganthan,et al. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. , 2011, Journal of theoretical biology.
[56] Naresh Manwani,et al. Geometric Decision Tree , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[57] Moni Naor,et al. Multiple Classifier Systems , 2013, Lecture Notes in Computer Science.
[58] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[59] Ponnuthurai N. Suganthan,et al. Random Forests with ensemble of feature spaces , 2014, Pattern Recognit..
[60] Ponnuthurai N. Suganthan,et al. Towards generating random forests via extremely randomized trees , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).
[61] Jon Atli Benediktsson,et al. Multiple Classifier Systems , 2015, Lecture Notes in Computer Science.