Hands on Pattern Recognition

[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.