Hands on Pattern Recognition
暂无分享,去创建一个
Hugo Jair Escalante | Manuel Montes | E. Sucar | H. Escalante | E. Sucar | Manuel Montes | M. Montes
[1] D. Bonchev. Chemical Graph Theory: Introduction and Fundamentals , 1991 .
[2] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[3] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[4] Ronald L. Graham,et al. Problem #7 , 1974, SIGS.
[5] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[6] Gavin C. Cawley,et al. Generalised Kernel Machines , 2007, 2007 International Joint Conference on Neural Networks.
[7] Mineichi Kudo,et al. Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..
[8] Susan A. Murphy,et al. Monographs on statistics and applied probability , 1990 .
[9] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[10] Yann LeCun,et al. Efficient Pattern Recognition Using a New Transformation Distance , 1992, NIPS.
[11] J. Langford. Tutorial on Practical Prediction Theory for Classification , 2005, J. Mach. Learn. Res..
[12] M. I. Jordan. Leo Breiman , 2011, 1101.0929.
[13] Gavin C. Cawley,et al. Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers , 2003, Pattern Recognit..
[14] Peter L. Bartlett,et al. For Valid Generalization the Size of the Weights is More Important than the Size of the Network , 1996, NIPS.
[15] Robert Tibshirani,et al. The Entire Regularization Path for the Support Vector Machine , 2004, J. Mach. Learn. Res..
[16] Adrian E. Raftery,et al. Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .
[17] Sayan Mukherjee,et al. Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.
[18] 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.
[19] M. Razinger,et al. Extended connectivity in chemical graphs , 1982 .
[20] Yadolah Dodge,et al. Mathematical Programming In Statistics , 1981 .
[21] Marc Boullé. A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes , 2005, J. Mach. Learn. Res..
[22] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[23] R. Strausberg,et al. From Knowing to Controlling: A Path from Genomics to Drugs Using Small Molecule Probes , 2003, Science.
[24] Samy Bengio,et al. Invariances in kernel methods: From samples to objects , 2006, Pattern Recognit. Lett..
[25] Ingo Steinwart,et al. Consistency and robustness of kernel based regression , 2005 .
[26] Jörg D. Wichard,et al. Agnostic Learning with Ensembles of Classifiers , 2007, 2007 International Joint Conference on Neural Networks.
[27] Ji Zhu,et al. Boosting as a Regularized Path to a Maximum Margin Classifier , 2004, J. Mach. Learn. Res..
[28] Hugo Jair Escalante,et al. Particle Swarm Model Selection , 2009, J. Mach. Learn. Res..
[29] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[30] André Elisseeff,et al. Stability and Generalization , 2002, J. Mach. Learn. Res..
[31] H. L. Morgan. The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. , 1965 .
[32] G. Cawley,et al. Efficient approximate leave-one-out cross-validation for kernel logistic regression , 2008, Machine Learning.
[33] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[34] John Moody,et al. Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.
[35] Yoshua Bengio,et al. No Unbiased Estimator of the Variance of K-Fold Cross-Validation , 2003, J. Mach. Learn. Res..
[36] Andreas Christmann,et al. On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition , 2004, J. Mach. Learn. Res..
[37] T. Hassard,et al. Applied Linear Regression , 2005 .
[38] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[39] OpitzDavid,et al. Popular ensemble methods , 1999 .
[40] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[41] David Rogers,et al. Cheminformatics analysis and learning in a data pipelining environment , 2006, Molecular Diversity.
[42] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[43] Andrew R. Leach,et al. An Introduction to Chemoinformatics , 2003 .
[44] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[45] B. Scholkopf,et al. Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).
[46] A. Garrett,et al. Ockham’s Razor , 1991 .
[47] Alexander Gammerman,et al. Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.
[48] Arthur E. Hoerl,et al. Application of ridge analysis to regression problems , 1962 .
[49] Bin Yu,et al. Model Selection and the Principle of Minimum Description Length , 2001 .
[50] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[51] R. C. Williamson,et al. Regularized principal manifolds , 2001 .
[52] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[53] Stephen A. Billings,et al. Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm , 2002, Neural Networks.
[54] Vladimir Vapnik,et al. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .
[55] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[56] Irene A. Stegun,et al. Handbook of Mathematical Functions. , 1966 .
[57] Liefeng Bo,et al. Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation , 2006 .
[58] Herman J. Bierens. Maximum Likelihood Theory , 2004 .
[59] Alon Orlitsky,et al. Supervised dimensionality reduction using mixture models , 2005, ICML.
[60] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[61] Juha Reunanen,et al. Model Selection and Assessment Using Cross-indexing , 2007, 2007 International Joint Conference on Neural Networks.
[62] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[63] Matthias W. Seeger,et al. PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification , 2003, J. Mach. Learn. Res..
[64] Ching Y. Suen,et al. Automatic model selection for the optimization of SVM kernels , 2005, Pattern Recognit..
[65] Marc Boullé,et al. MODL: A Bayes optimal discretization method for continuous attributes , 2006, Machine Learning.
[66] Y. Chen. [The change of serum alpha 1-antitrypsin level in patients with spontaneous pneumothorax]. , 1995, Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases.
[67] Pierre Baldi,et al. Lossless Compression of Chemical Fingerprints Using Integer Entropy Codes Improves Storage and Retrieval , 2007, J. Chem. Inf. Model..
[68] R. Rakotomalala. Graphes d'induction , 1997 .
[69] M. R. Mickey,et al. Estimation of Error Rates in Discriminant Analysis , 1968 .
[70] G. V. Kass. An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .
[71] Saharon Shelah,et al. Black Boxes , 2008, 0812.0656.
[72] Olivier Chapelle,et al. Model Selection for Support Vector Machines , 1999, NIPS.
[73] S. Unger. Molecular Connectivity in Structure–activity Analysis , 1987 .
[74] Yoshua Bengio,et al. Gradient-Based Optimization of Hyperparameters , 2000, Neural Computation.
[75] Martin Sewell. Structural Risk Minimization , 2008 .
[76] Kenneth Steiglitz,et al. Combinatorial Optimization: Algorithms and Complexity , 1981 .
[77] Kristin P. Bennett,et al. A Pattern Search Method for Model Selection of Support Vector Regression , 2002, SDM.
[78] Isabelle Guyon,et al. Design and Analysis of the Causation and Prediction Challenge , 2008, WCCI Causation and Prediction Challenge.
[79] G. Wahba. Spline models for observational data , 1990 .
[80] Gavin C. Cawley,et al. Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[81] T. Ho,et al. Data Complexity in Pattern Recognition , 2006 .
[82] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[83] Nello Cristianini,et al. Controlling the Sensitivity of Support Vector Machines , 1999 .
[84] Pierre Baldi,et al. One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties , 2007, J. Chem. Inf. Model..
[85] Alexander Zien,et al. Semi-Supervised Learning , 2006 .
[86] Constantin F. Aliferis,et al. HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection , 2003, AMIA.
[87] Enrique Vidal,et al. Bernoulli mixture models for binary images , 2004, ICPR 2004.
[88] Ethem Alpaydin,et al. Introduction to machine learning , 2004, Adaptive computation and machine learning.
[89] Bernhard Schölkopf,et al. Use of the Zero-Norm with Linear Models and Kernel Methods , 2003, J. Mach. Learn. Res..
[90] C. Gold,et al. Fast Bayesian support vector machine parameter tuning with the Nystrom method , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[91] Isabelle Guyon,et al. A Stability Based Method for Discovering Structure in Clustered Data , 2001, Pacific Symposium on Biocomputing.
[92] Ching Y. Suen,et al. Empirical error based optimization of SVM kernels: application to digit image recognition , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.
[93] Bernhard Schölkopf,et al. Dynamic Alignment Kernels , 2000 .
[94] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[95] Pat Langley,et al. Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..
[96] David S. Wishart. Number of Clusters , 2005 .
[97] Georg Dengler. N.F. [1.] , 1873 .
[98] A. N. Tikhonov,et al. Solutions of ill-posed problems , 1977 .
[99] Florian Steinke,et al. Bayesian Inference and Optimal Design in the Sparse Linear Model , 2007, AISTATS.
[100] Pierre Baldi,et al. ChemDB update - full-text search and virtual chemical space , 2007, Bioinform..
[101] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[102] Vladimir Vapnik,et al. Principles of Risk Minimization for Learning Theory , 1991, NIPS.
[103] T Poggio,et al. Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.
[104] V. Vapnik. Estimation of Dependences Based on Empirical Data , 2006 .
[105] C. E. SHANNON,et al. A mathematical theory of communication , 1948, MOCO.
[106] Mark A. Pitt,et al. Advances in Minimum Description Length: Theory and Applications , 2005 .
[107] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.
[108] Lorenzo Rosasco,et al. Some Properties of Regularized Kernel Methods , 2004, J. Mach. Learn. Res..
[109] Andreas Wendemuth,et al. Kernel Least-Squares Models Using Updates of the Pseudoinverse , 2006, Neural Computation.
[110] Joachim M. Buhmann,et al. PerformancePrediction Challenge , 2006, IJCNN.
[111] Christian Igel,et al. Multi-objective Model Selection for Support Vector Machines , 2005, EMO.
[112] M. Stone. Asymptotics for and against cross-validation , 1977 .
[113] Brian D. Ripley,et al. Pattern Recognition and Neural Networks , 1996 .
[114] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[115] Roman W. Lutz,et al. LogitBoost with Trees Applied to the WCCI 2006 Performance Prediction Challenge Datasets , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[116] K. Humbel,et al. Chemical Applications of Topology and Graph Theory, R.B. King (Ed.). Elsevier Science Publishers, Amsterdam (1983), (ISBN 0-444-42244-7). XII + 494 p. Price Dfl. 275.00 , 1985 .
[117] J. Hartigan. Direct Clustering of a Data Matrix , 1972 .
[118] Glenn Fung,et al. A Feature Selection Newton Method for Support Vector Machine Classification , 2004, Comput. Optim. Appl..
[119] R. Tibshirani,et al. An introduction to the bootstrap , 1993 .
[120] Bernhard Schölkopf,et al. Learning with kernels , 2001 .
[121] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[122] D. Rogers,et al. Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up , 2005, Journal of biomolecular screening.
[123] Genshiro Kitagawa,et al. Bayesian Information Criteria , 2008 .
[124] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[125] Pierre Hansen,et al. Variable neighborhood search: Principles and applications , 1998, Eur. J. Oper. Res..
[126] L. Fernholz. von Mises Calculus For Statistical Functionals , 1983 .
[127] Isabelle Guyon,et al. Causality : Objectives and Assessment , 2010 .
[128] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[129] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[130] Thomas G. Dietterich. Adaptive computation and machine learning , 1998 .
[131] Pierre Baldi,et al. Graph kernels for chemical informatics , 2005, Neural Networks.
[132] H. B. Barlow,et al. Unsupervised Learning , 1989, Neural Computation.
[133] R. Kil,et al. Model Selection for Regression with Continuous Kernel Functions Using the Modulus of Continuity , 2008 .
[134] Isabelle Guyon,et al. Design and analysis of the KDD cup 2009: fast scoring on a large orange customer database , 2009, SKDD.
[135] Gavin C. Cawley,et al. Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines , 2007, 2007 International Joint Conference on Neural Networks.
[136] V. Vapnik,et al. Bounds on Error Expectation for Support Vector Machines , 2000, Neural Computation.
[137] Taeho Jo,et al. A Multiple Resampling Method for Learning from Imbalanced Data Sets , 2004, Comput. Intell..
[138] Senjian An,et al. Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression , 2007, Pattern Recognit..
[139] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[140] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[141] Samy Bengio,et al. SVMTorch: Support Vector Machines for Large-Scale Regression Problems , 2001, J. Mach. Learn. Res..
[142] Robert D. Nowak,et al. Unlabeled data: Now it helps, now it doesn't , 2008, NIPS.
[143] Chih-Jen Lin,et al. Radius Margin Bounds for Support Vector Machines with the RBF Kernel , 2002, Neural Computation.
[144] K. Sen,et al. Molecular Similarity II , 1995 .
[145] Jason Weston. Leave-One-Out Support Vector Machines , 1999, IJCAI.
[146] Gunnar Rätsch,et al. Soft Margins for AdaBoost , 2001, Machine Learning.
[147] Tomaso A. Poggio,et al. Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..
[148] Ingo Steinwart,et al. On the Optimal Parameter Choice for v-Support Vector Machines , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[149] Peter Willett,et al. Promoting Access to White Rose Research Papers Effectiveness of Graph-based and Fingerprint-based Similarity Measures for Virtual Screening of 2d Chemical Structure Databases , 2022 .
[150] Ran El-Yaniv,et al. Distributional Word Clusters vs. Words for Text Categorization , 2003, J. Mach. Learn. Res..
[151] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[152] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[153] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[154] Jing Hu,et al. Model Selection via Bilevel Optimization , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[155] Partha Niyogi,et al. Almost-everywhere Algorithmic Stability and Generalization Error , 2002, UAI.
[156] Christophe Croux,et al. An Information Criterion for Variable Selection in Support Vector Machines , 2007 .
[157] M. Stone. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[158] John A. Nelder,et al. A Simplex Method for Function Minimization , 1965, Comput. J..
[159] Jianying Hu,et al. Winning the KDD Cup Orange Challenge with Ensemble Selection , 2009, KDD Cup.
[160] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[161] Weifeng Liu,et al. Adaptive and Learning Systems for Signal Processing, Communication, and Control , 2010 .
[162] Carl Gold,et al. Bayesian approach to feature selection and parameter tuning for support vector machine classifiers , 2005, Neural Networks.
[163] R. Rifkin,et al. Notes on Regularized Least Squares , 2007 .
[164] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[165] Fenguangzhai Song. CD , 1992 .
[166] Marc Boullé,et al. A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning , 2007, J. Mach. Learn. Res..
[167] M. Kearns,et al. Algorithmic stability and sanity-check bounds for leave-one-out cross-validation , 1999 .
[168] Angela Montanari. Linear Discriminant Analysis and Transvariation , 2004, J. Classif..
[169] Masoud Nikravesh,et al. Feature Extraction - Foundations and Applications , 2006, Feature Extraction.
[170] Alexander J. Smola,et al. Fast Kernels for String and Tree Matching , 2002, NIPS.
[171] Nello Cristianini,et al. An introduction to Support Vector Machines , 2000 .
[172] Ming Li,et al. An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.