Prediction based on averages over automatically induced learners: ensemble methods and Bayesian techniques

Tesis doctoral inedita. Universidad Autonoma de Madrid, Escuela Politecnica Superior, noviembre de 2009

[1]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[2]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[3]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[4]  M. Abramowitz,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[5]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[6]  S. Adler.  Over-relaxation method for the Monte Carlo evaluation of the partition function for multiquadratic actions , 1981 .

[7]  Yoav Freund,et al.  Boosting a weak learning algorithm by majority , 1995, COLT '90.

[8]  John F. Kolen,et al.  Backpropagation is Sensitive to Initial Conditions , 1990, Complex Syst..

[9]  R. T. Cox.  Probability, frequency and reasonable expectation , 1990 .

[10]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[12]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[13]  Anders Krogh,et al.  A Simple Weight Decay Can Improve Generalization , 1991, NIPS.

[14]  Trevor Hastie,et al.  Statistical Models in S , 1991 .

[15]  J. Kittler,et al.  Multistage pattern recognition with reject option , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[16]  L. Cooper,et al.  When Networks Disagree: Ensemble Methods for Hybrid Neural Networks , 1992 .

[17]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[18]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[19]  Ludmila I. Kuncheva,et al.  Genetic Algorithm for Feature Selection for Parallel Classifiers , 1993, Inf. Process. Lett..

[20]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Anders Krogh,et al.  Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.

[22]  Cullen Schaffer,et al.  A Conservation Law for Generalization Performance , 1994, ICML.

[23]  W. Press,et al.  Numerical Recipes in Fortran: The Art of Scientific Computing.@@@Numerical Recipes in C: The Art of Scientific Computing. , 1994 .

[24]  M. Kamel,et al.  Voting schemes for cooperative neural network classifiers , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[25]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[26]  David P. Williamson,et al.  Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming , 1995, JACM.

[27]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[28]  Anders Krogh,et al.  Learning with ensembles: How overfitting can be useful , 1995, NIPS.

[29]  Naonori Ueda,et al.  Generalization error of ensemble estimators , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[30]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[31]  Amanda J. C. Sharkey,et al.  On Combining Artificial Neural Nets , 1996, Connect. Sci..

[32]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[33]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[34]  Bruce E. Rosen,et al.  Ensemble Learning Using Decorrelated Neural Networks , 1996, Connect. Sci..

[35]  David Barber,et al.  Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo , 1996, NIPS.

[36]  R. Tibshirani,et al.  Combining Estimates in Regression and Classification , 1996 .

[37]  Kagan Tumer,et al.  Error Correlation and Error Reduction in Ensemble Classifiers , 1996, Connect. Sci..

[38]  David H. Wolpert,et al.  The Lack of A Priori Distinctions Between Learning Algorithms , 1996, Neural Computation.

[39]  L. Breiman.  OUT-OF-BAG ESTIMATION , 1996 .

[40]  Kevin W. Bowyer,et al.  Combination of Multiple Classifiers Using Local Accuracy Estimates , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Thomas G. Dietterich.  Machine-Learning Research , 1997, AI Mag..

[42]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[43]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[44]  Noel E. Sharkey,et al.  Combining diverse neural nets , 1997, The Knowledge Engineering Review.

[45]  Harris Drucker,et al.  Improving Regressors using Boosting Techniques , 1997, ICML.

[46]  Radford M. Neal.  Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification , 1997, physics/9701026.

[47]  Ching Y. Suen,et al.  Application of majority voting to pattern recognition: an analysis of its behavior and performance , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[48]  Thomas G. Dietterich,et al.  Pruning Adaptive Boosting , 1997, ICML.

[49]  Robert E. Schapire,et al.  Using output codes to boost multiclass learning problems , 1997, ICML.

[50]  E. George,et al.  APPROACHES FOR BAYESIAN VARIABLE SELECTION , 1997 .

[51]  Yali Amit,et al.  Shape Quantization and Recognition with Randomized Trees , 1997, Neural Computation.

[52]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[53]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[54]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[55]  David Barber,et al.  Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[56]  Amanda J. C. Sharkey,et al.  Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems , 1999 .

[57]  Alexey Tsymbal,et al.  A Dynamic Integration Algorithm for an Ensemble of Classifiers , 1999, ISMIS.

[58]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[59]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[60]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[61]  Xin Yao,et al.  Ensemble learning via negative correlation , 1999, Neural Networks.

[62]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[63]  Nathan Intrator,et al.  Boosting Regression Estimators , 1999, Neural Computation.

[64]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[65]  Hagai Attias,et al.  A Variational Bayesian Framework for Graphical Models , 1999 .

[66]  Leo Breiman,et al.  Prediction Games and Arcing Algorithms , 1999, Neural Computation.

[67]  Nikunj C. Oza,et al.  Decimated input ensembles for improved generalization , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[68]  Roger E Bumgarner,et al.  Comparative hybridization of an array of 21,500 ovarian cDNAs for the discovery of genes overexpressed in ovarian carcinomas. , 1999, Gene.

[69]  Thomas Richardson,et al.  Boosting methodology for regression problems , 1999, AISTATS.

[70]  Christino Tamon,et al.  On the Boosting Pruning Problem , 2000, ECML.

[71]  Alexey Tsymbal,et al.  Bagging and Boosting with Dynamic Integration of Classifiers , 2000, PKDD.

[72]  Thomas G. Dietterich.  Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.

[73]  Fabio Roli,et al.  Design of effective multiple classifier systems by clustering of classifiers , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[74]  David J. C. MacKay,et al.  Variational Gaussian process classifiers , 2000, IEEE Trans. Neural Networks Learn. Syst..

[75]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[76]  Pedro M. Domingos.  A Unifeid Bias-Variance Decomposition and its Applications , 2000, ICML.

[77]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[78]  Ole Winther,et al.  Gaussian Processes for Classification: Mean-Field Algorithms , 2000, Neural Computation.

[79]  Robert P. W. Duin,et al.  Experiments with Classifier Combining Rules , 2000, Multiple Classifier Systems.

[80]  Fabio Roli,et al.  Dynamic classifier selection based on multiple classifier behaviour , 2001, Pattern Recognit..

[81]  Tom Minka,et al.  Expectation Propagation for approximate Bayesian inference , 2001, UAI.

[82]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[83]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[84]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..

[85]  Colin Campbell,et al.  Bayes Point Machines , 2001, J. Mach. Learn. Res..

[86]  Edward R. Dougherty,et al.  Small Sample Issues for Microarray-Based Classification , 2001, Comparative and functional genomics.

[87]  Ole Winther,et al.  TAP Gibbs Free Energy, Belief Propagation and Sparsity , 2001, NIPS.

[88]  Olivier Debeir,et al.  Limiting the Number of Trees in Random Forests , 2001, Multiple Classifier Systems.

[89]  Zoran Obradovic,et al.  Effective pruning of neural network classifier ensembles , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[90]  Shlomo Argamon,et al.  Arbitrating Among Competing Classifiers Using Learned Referees , 2001, Knowledge and Information Systems.

[91]  Tom Minka,et al.  A family of algorithms for approximate Bayesian inference , 2001 .

[92]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[93]  E. Dougherty,et al.  Gene-expression profiles in hereditary breast cancer. , 2001, The New England journal of medicine.

[94]  R. Spang,et al.  Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[95]  Salvatore J. Stolfo,et al.  Cost Complexity-Based Pruning of Ensemble Classifiers , 2001, Knowledge and Information Systems.

[96]  Fabio Roli,et al.  An approach to the automatic design of multiple classifier systems , 2001, Pattern Recognit. Lett..

[97]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[98]  Yi Li,et al.  Bayesian automatic relevance determination algorithms for classifying gene expression data. , 2002, Bioinformatics.

[99]  Jiawei Zhang,et al.  An improved rounding method and semidefinite programming relaxation for graph partition , 2002, Math. Program..

[100]  David J. Spiegelhalter,et al.  VIBES: A Variational Inference Engine for Bayesian Networks , 2002, NIPS.

[101]  Gunnar Rätsch,et al.  An Introduction to Boosting and Leveraging , 2002, Machine Learning Summer School.

[102]  Philip S. Yu,et al.  Pruning and dynamic scheduling of cost-sensitive ensembles , 2002, AAAI/IAAI.

[103]  Peter Bühlmann,et al.  Supervised clustering of genes , 2002, Genome Biology.

[104]  Giorgio Valentini,et al.  Ensembles of Learning Machines , 2002, WIRN.

[105]  Lehel Csató,et al.  Sparse On-Line Gaussian Processes , 2002, Neural Computation.

[106]  Johannes Fürnkranz,et al.  Round Robin Classification , 2002, J. Mach. Learn. Res..

[107]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[108]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

[109]  Geoffrey J McLachlan,et al.  Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[110]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[111]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[112]  Robert P. W. Duin,et al.  Bagging, Boosting and the Random Subspace Method for Linear Classifiers , 2002, Pattern Analysis & Applications.

[113]  Neil D. Lawrence,et al.  Fast Sparse Gaussian Process Methods: The Informative Vector Machine , 2002, NIPS.

[114]  Christopher M. Bishop,et al.  Bayesian Hierarchical Mixtures of Experts , 2002, UAI.

[115]  Hyun-Chul Kim,et al.  Constructing support vector machine ensemble , 2003, Pattern Recognit..

[116]  Kagan Tumer,et al.  Input decimated ensembles , 2003, Pattern Analysis & Applications.

[117]  Robert P. W. Duin,et al.  Limits on the majority vote accuracy in classifier fusion , 2003, Pattern Analysis & Applications.

[118]  Matthias W. Seeger,et al.  Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations , 2003 .

[119]  E. Lander,et al.  A molecular signature of metastasis in primary solid tumors , 2003, Nature Genetics.

[120]  Carla E. Brodley,et al.  Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach , 2003, ICML.

[121]  Mykola Pechenizkiy,et al.  Dynamic Integration of Classifiers in the Space of Principal Components , 2003, ADBIS.

[122]  Marina Vannucci,et al.  Gene selection: a Bayesian variable selection approach , 2003, Bioinform..

[123]  Wei Tang,et al.  Selective Ensemble of Decision Trees , 2003, RSFDGrC.

[124]  Lorenza Saitta,et al.  Monte Carlo theory as an explanation of bagging and boosting , 2003, IJCAI 2003.

[125]  Tom Heskes,et al.  Clustering ensembles of neural network models , 2003, Neural Networks.

[126]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[127]  D. Edwards,et al.  Statistical Analysis of Gene Expression Microarray Data , 2003 .

[128]  P. Bühlmann.  Bagging, subagging and bragging for improving some prediction algorithms , 2003 .

[129]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[130]  Leo Breiman,et al.  Randomizing Outputs to Increase Prediction Accuracy , 2000, Machine Learning.

[131]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[132]  Grigorios Tsoumakas,et al.  Effective Voting of Heterogeneous Classifiers , 2004, ECML.

[133]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[134]  Stephen T. C. Wong,et al.  Cancer classification and prediction using logistic regression with Bayesian gene selection , 2004, J. Biomed. Informatics.

[135]  Rich Caruana,et al.  Ensemble selection from libraries of models , 2004, ICML.

[136]  Bani K. Mallick,et al.  Gene selection using a two-level hierarchical Bayesian model , 2004, Bioinform..

[137]  Tom Bylander,et al.  Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates , 2002, Machine Learning.

[138]  João Gama,et al.  Cascade Generalization , 2000, Machine Learning.

[139]  Geoffrey I. Webb,et al.  MultiBoosting: A Technique for Combining Boosting and Wagging , 2000, Machine Learning.

[140]  Lawrence Carin,et al.  Joint Classifier and Feature Optimization for Comprehensive Cancer Diagnosis Using Gene Expression Data , 2004, J. Comput. Biol..

[141]  Saso Dzeroski,et al.  Combining Classifiers with Meta Decision Trees , 2003, Machine Learning.

[142]  Marcel Dettling,et al.  BagBoosting for tumor classification with gene expression data , 2004, Bioinform..

[143]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[144]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[145]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[146]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[147]  Cigdem Demir,et al.  Cost-conscious classifier ensembles , 2005, Pattern Recognit. Lett..

[148]  Fabio Roli,et al.  A theoretical and experimental analysis of linear combiners for multiple classifier systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[149]  Gunnar Rätsch,et al.  Efficient Margin Maximizing with Boosting , 2005, J. Mach. Learn. Res..

[150]  Bogdan Gabrys,et al.  Classifier selection for majority voting , 2005, Inf. Fusion.

[151]  Timothy S Gardner,et al.  Reverse-engineering transcription control networks. , 2005, Physics of life reviews.

[152]  Xin Yao,et al.  Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.

[153]  Carl E. Rasmussen,et al.  Assessing Approximate Inference for Binary Gaussian Process Classification , 2005, J. Mach. Learn. Res..

[154]  Robert P. W. Duin,et al.  Combining Feature Subsets in Feature Selection , 2005, Multiple Classifier Systems.

[155]  L. Breiman.  Stacked Regressions , 1996, Machine Learning.

[156]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[157]  P. Hall,et al.  Properties of bagged nearest neighbour classifiers , 2005 .

[158]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[159]  Peter Tiño,et al.  Managing Diversity in Regression Ensembles , 2005, J. Mach. Learn. Res..

[160]  Gonzalo Martínez-Muñoz,et al.  Switching class labels to generate classification ensembles , 2005, Pattern Recognit..

[161]  Lawrence O. Hall,et al.  Ensemble diversity measures and their application to thinning , 2004, Inf. Fusion.

[162]  Jae Won Lee,et al.  An extensive comparison of recent classification tools applied to microarray data , 2004, Comput. Stat. Data Anal..

[163]  Igor V. Tetko,et al.  Gene selection from microarray data for cancer classification - a machine learning approach , 2005, Comput. Biol. Chem..

[164]  Ole Winther,et al.  Expectation Consistent Approximate Inference , 2005, J. Mach. Learn. Res..

[165]  Lefteris Angelis,et al.  Selective fusion of heterogeneous classifiers , 2005, Intell. Data Anal..

[166]  Josef Kittler,et al.  Combining classifiers: A theoretical framework , 1998, Pattern Analysis and Applications.

[167]  Anand M. Narasimhamurthy.  Theoretical bounds of majority voting performance for a binary classification problem , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[168]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[169]  Huanhuan Chen,et al.  A Probabilistic Ensemble Pruning Algorithm , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[170]  Aníbal R. Figueiras-Vidal,et al.  Boosting by weighting critical and erroneous samples , 2006, Neurocomputing.

[171]  William Nick Street,et al.  Ensemble Pruning Via Semi-definite Programming , 2006, J. Mach. Learn. Res..

[172]  Gavin C. Cawley,et al.  Gene Selection in Cancer Classification using Sparse Logistic Regression with Bayesian Regularisation , 2006 .

[173]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[174]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[175]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[176]  Daniel Hernández-Lobato,et al.  Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm , 2006, IDEAL.

[177]  Gonzalo Martínez-Muñoz,et al.  Pruning in ordered bagging ensembles , 2006, ICML.

[178]  Grigorios Tsoumakas,et al.  Ensemble Pruning Using Reinforcement Learning , 2006, SETN.

[179]  Daniel Hernández-Lobato,et al.  Pruning in Ordered Regression Bagging Ensembles , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[180]  Michael I. Jordan,et al.  Variational inference for Dirichlet process mixtures , 2006 .

[181]  Gonzalo Martínez-Muñoz,et al.  Using boosting to prune bagging ensembles , 2007, Pattern Recognit. Lett..

[182]  Sotiris B. Kotsiantis,et al.  Machine learning: a review of classification and combining techniques , 2006, Artificial Intelligence Review.

[183]  Anne M. P. Canuto,et al.  Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles , 2007, Pattern Recognit. Lett..

[184]  Patrick P. K. Chan,et al.  Neural network ensemble pruning using sensitivity measure in web applications , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[185]  Ludmila I. Kuncheva,et al.  A stability index for feature selection , 2007, Artificial Intelligence and Applications.

[186]  Florian Steinke,et al.  Bayesian Inference and Optimal Design in the Sparse Linear Model , 2007, AISTATS.

[187]  Daniel Hernández-Lobato,et al.  Selection of Decision Stumps in Bagging Ensembles , 2007, ICANN.

[188]  Jean-Philippe Thiran,et al.  Information Theoretic Combination of Classifiers with Application to AdaBoost , 2007, MCS.

[189]  Lawrence O. Hall,et al.  A Comparison of Decision Tree Ensemble Creation Techniques , 2007 .

[190]  Gene H. Golub,et al.  Methods for modifying matrix factorizations , 1972, Milestones in Matrix Computation.

[191]  Daniel Hernández-Lobato,et al.  Out of Bootstrap Estimation of Generalization Error Curves in Bagging Ensembles , 2007, IDEAL.

[192]  Hongzhe Li,et al.  In Response to Comment on "Network-constrained regularization and variable selection for analysis of genomic data" , 2008, Bioinform..

[193]  Thomas G. Dietterich,et al.  Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms , 2008 .

[194]  Grigorios Tsoumakas,et al.  Greedy regression ensemble selection: Theory and an application to water quality prediction , 2008, Inf. Sci..

[195]  Fabio Roli,et al.  A Theoretical Analysis of Bagging as a Linear Combination of Classifiers , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[196]  Aníbal R. Figueiras-Vidal,et al.  A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles , 2008, IEEE Transactions on Neural Networks.

[197]  Daniel Hernández-Lobato,et al.  Bayes Machines for binary classification , 2008, Pattern Recognit. Lett..

[198]  Daniel Hernández-Lobato.  Sparse Bayes Machines for Binary Classification , 2008, ICANN.

[199]  Daniel Hernández-Lobato,et al.  An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[200]  Laurent Heutte,et al.  Influence of Hyperparameters on Random Forest Accuracy , 2009, MCS.

[201]  Yehuda Koren,et al.  The BellKor Solution to the Netflix Grand Prize , 2009 .

[202]  Wei Pan,et al.  Network-based support vector machine for classification of microarray samples , 2009, BMC Bioinformatics.

[203]  Yuan Qi,et al.  Virtual Vector Machine for Bayesian Online Classification , 2009, UAI.

[204]  Antanas Verikas,et al.  A feature selection technique for generation of classification committees and its application to categorization of laryngeal images , 2009, Pattern Recognit..

[205]  Yoav Freund,et al.  A more robust boosting algorithm , 2009, 0905.2138.

[206]  Daniel Hernández-Lobato,et al.  Expectation Propagation for microarray data classification , 2010, Pattern Recognit. Lett..

[207]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[208]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.