Learning Metrics and Discriminative Clustering
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[1] Samuel Kaski,et al. Bankruptcy analysis with self-organizing maps in learning metrics , 2001, IEEE Trans. Neural Networks.
[2] R. Tibshirani,et al. Discriminant Analysis by Gaussian Mixtures , 1996 .
[3] Samuel Kaski,et al. Clustering by Similarity in an Auxiliary Space , 2000, IDEAL.
[4] Shun-ichi Amari,et al. Differential-geometrical methods in statistics , 1985 .
[5] T. Heskes. Energy functions for self-organizing maps , 1999 .
[6] N. L. Johnson,et al. Linear Statistical Inference and Its Applications , 1966 .
[7] H. Hotelling. Relations Between Two Sets of Variates , 1936 .
[8] Naftali Tishby,et al. Unsupervised document classification using sequential information maximization , 2002, SIGIR '02.
[9] Trevor Hastie,et al. Flexible discriminant and mixture models , 2000 .
[10] Allen Gersho,et al. Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.
[11] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[12] Yizong Cheng,et al. Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[13] Naftali Tishby,et al. Sufficient Dimensionality Reduction , 2003, J. Mach. Learn. Res..
[14] Teuvo Kohonen,et al. Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.
[15] Volker Roth,et al. Nonlinear Discriminant Analysis Using Kernel Functions , 1999, NIPS.
[16] Samuel Kaski,et al. Discriminative clustering of text documents , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..
[17] Si Wu,et al. Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.
[18] Naftali Tishby,et al. Document clustering using word clusters via the information bottleneck method , 2000, SIGIR '00.
[19] Michael E. Tipping. Deriving cluster analytic distance functions from Gaussian mixture models , 1999 .
[20] E. Altman. Corporate financial distress : a complete guide to predicting, avoiding, and dealing with bankruptcy , 1983 .
[21] Thomas Hofmann,et al. Statistical Models for Co-occurrence Data , 1998 .
[22] Robert Tibshirani,et al. Discriminant Adaptive Nearest Neighbor Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[23] David Haussler,et al. Using the Fisher Kernel Method to Detect Remote Protein Homologies , 1999, ISMB.
[24] William M. Campbell,et al. Mutual Information in Learning Feature Transformations , 2000, ICML.
[25] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[26] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[27] Naftali Tishby,et al. Agglomerative Information Bottleneck , 1999, NIPS.
[28] Christopher M. Bishop,et al. GTM: A Principled Alternative to the Self-Organizing Map , 1996, NIPS.
[29] Juha Vesanto,et al. Data exploration process based on the self-organizing map , 2002 .
[30] Christopher M. Bishop,et al. Developments of the generative topographic mapping , 1998, Neurocomputing.
[31] M. Murray,et al. Differential Geometry and Statistics , 1993 .
[32] David E. Booth,et al. Applied Multivariate Analysis , 2003, Technometrics.
[33] Noam Slonim,et al. Maximum Likelihood and the Information Bottleneck , 2002, NIPS.
[34] I. Good. On the Application of Symmetric Dirichlet Distributions and their Mixtures to Contingency Tables , 1976 .
[35] Gal Chechik,et al. Extracting Relevant Structures with Side Information , 2002, NIPS.
[36] Samuel Kaski,et al. Discriminative Clustering: Optimal Contingency Tables by Learning Metrics , 2002, ECML.
[37] David Haussler,et al. Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.
[38] Samuel Kaski,et al. Learning More Accurate Metrics for Self-Organizing Maps , 2002, ICANN.
[39] Samuel Kaski,et al. A Topography-Preserving Latent Variable Model with Learning Metrics , 2001, WSOM.
[40] Samuel Kaski,et al. Clustering Based on Conditional Distributions in an Auxiliary Space , 2002, Neural Computation.
[41] Henry Tirri,et al. Unsupervised Bayesian visualization of high-dimensional data , 2000, KDD '00.
[42] Gregory R. Grant,et al. Bioinformatics - The Machine Learning Approach , 2000, Comput. Chem..
[43] Thomas Hofmann,et al. Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.
[44] Tommi S. Jaakkola,et al. Partially labeled classification with Markov random walks , 2001, NIPS.
[45] Roger Smith,et al. A history of psychology: main currents in psychological thought , 1982, Medical History.
[46] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[47] Michael I. Jordan,et al. Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.
[48] Thomas Hofmann,et al. Learning from Dyadic Data , 1998, NIPS.
[49] B. Efron. The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis , 1975 .
[50] Yoshua Bengio,et al. Pattern Recognition and Neural Networks , 1995 .
[51] C. R. Rao,et al. Information and the Accuracy Attainable in the Estimation of Statistical Parameters , 1992 .
[52] Shun-ichi Amari,et al. Methods of information geometry , 2000 .
[53] Samuel Kaski,et al. Learning Metrics for Visualizing Gene Functional Similarities , 2002 .
[54] K. Torkkola,et al. Nonlinear feature transforms using maximum mutual information , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).
[55] James J. Filliben,et al. NIST/SEMATECH e-Handbook of Statistical Methods; Chapter 1: Exploratory Data Analysis , 2003 .
[56] M. Braga,et al. Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..
[57] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[58] Samuel Kaski,et al. Principle of Learning Metrics for Exploratory Data Analysis , 2004, J. VLSI Signal Process..
[59] Henry Tirri,et al. Supervised model-based visualization of high-dimensional data , 2000, Intell. Data Anal..
[60] R. T. Cox. Probability, frequency and reasonable expectation , 1990 .
[61] Samuel Kaski,et al. Discriminative Clustering: Vector Quantization in Learning Metrics , 2003 .
[62] Jim Kay,et al. Feature discovery under contextual supervision using mutual information , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.
[63] Samuel Kaski,et al. Regularized discriminative clustering , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).
[64] A. I.,et al. Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks , 2023, Biology.
[65] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[66] Léon Bottou,et al. On-line learning and stochastic approximations , 1999 .
[67] Kimmo Kiviluoto,et al. Predicting bankruptcies with the self-organizing map , 1998, Neurocomputing.
[68] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[69] P. Groenen,et al. Modern multidimensional scaling , 1996 .
[70] Naftali Tishby,et al. Data Clustering by Markovian Relaxation and the Information Bottleneck Method , 2000, NIPS.
[71] Edward I. Altman,et al. FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .
[72] Wray L. Buntine. Variational Extensions to EM and Multinomial PCA , 2002, ECML.
[73] Teuvo Kohonen,et al. Self-Organization and Associative Memory , 1988 .
[74] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[75] E. T. Jaynes,et al. Where do we Stand on Maximum Entropy , 1979 .
[76] Sebastian Thrun,et al. Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.
[77] Samuel Kaski,et al. Discriminative Clustering in Fisher Metrics , 2003 .
[78] Kari Torkkola,et al. Learning Discriminative Feature Transforms to Low Dimensions in Low Dimentions , 2001, NIPS.
[79] Naftali Tishby,et al. The information bottleneck method , 2000, ArXiv.
[80] Dimitrios Gunopulos,et al. Locally Adaptive Metric Nearest-Neighbor Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[81] Naftali Tishby,et al. Distributional Clustering of English Words , 1993, ACL.
[82] William H. Press,et al. Numerical recipes in C. The art of scientific computing , 1987 .
[83] Anil K. Jain,et al. Algorithms for Clustering Data , 1988 .
[84] R. Kass,et al. Geometrical Foundations of Asymptotic Inference , 1997 .
[85] J. E. Glynn,et al. Numerical Recipes: The Art of Scientific Computing , 1989 .
[86] William H. Press,et al. The Art of Scientific Computing Second Edition , 1998 .
[87] Trevor J. Hastie,et al. Discriminative vs Informative Learning , 1997, KDD.
[88] Ted Chang. Geometrical foundations of asymptotic inference , 2002 .
[89] G. Baudat,et al. Generalized Discriminant Analysis Using a Kernel Approach , 2000, Neural Computation.
[90] L. A. Goodman. The Analysis of Cross-Classified Data Having Ordered and/or Unordered Categories: Association Models, Correlation Models, and Asymmetry Models for Contingency Tables With or Without Missing Entries , 1985 .
[91] J.C. Principe,et al. A methodology for information theoretic feature extraction , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).
[92] Michael I. Jordan,et al. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.
[93] Dimitrios Gunopulos,et al. An Adaptive Metric Machine for Pattern Classification , 2000, NIPS.
[94] Zoubin Ghahramani,et al. Probabilistic Models for Unsupervised Learning , 1999 .
[95] A. Dale Magoun,et al. Decision, estimation and classification , 1989 .
[96] Michael McGill,et al. Introduction to Modern Information Retrieval , 1983 .
[97] D. Botstein,et al. Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[98] Geoffrey Hunter. What Computers Can't Do , 1988, Philosophy.
[99] Naftali Tishby,et al. Objective Classification of Galaxy Spectra using the Information Bottleneck Method , 2000, astro-ph/0005306.