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Group: LTN - library [98 articles]

Neue Artikel von Mitliedern der Gruppe LTN Gruppe
  • On Convergence Properties of the EM Algorithm for Gaussian Mixtures
    Neural Computation, Vol. 8, No. 1. (1996), pp. 129-151.
    by Lei Xu, Michael I Jordan
    posted to mixture em by ecome to the group LTN on 2007-06-18 10:35:45 as read
  • Deterministic annealing for clustering, compression, classification, regression, and related optimization problems
    (1998)
    by K Rose
    posted to em annealing by ecome to the group LTN on 2007-05-23 10:04:58 as ** along with 2 people geomblog anon_pl
  • Mixture-model-based signal denoising
    Advances in Data Analysis and Classification, Vol. 1, No. 1. (23 March 2007), pp. 39-51.
    by Allou Samé, Latifa Oukhellou, Etienne Côme, Patrice Aknin
    posted to regression mixture em by ecome to the group LTN on 2007-04-11 10:23:39 as read
  • The Elements of Statistical Learning, Data Mining, Inference and Prediction
    by Trevor, Tibshirani Robert, Jerome Friedman
    posted to spline regression kernel by ecome to the group LTN on 2007-04-05 13:57:30 as **
  • Mixtures of principal component analyzers
    (1997)
    by M Tipping, C Bishop
    posted to pca mixture by ecome to the group LTN on 2007-04-05 13:49:16 as ** along with 1 person scotto36
  • GTM: The Generative Topographic Mapping
    Neural Computation, Vol. 10, No. 1. (1998), pp. 215-234.
    by Christopher M Bishop, Markus Svensen, Christopher KI Williams
    posted to gtm em by ecome to the group LTN on 2007-04-05 13:48:53 as ** along with 3 people ngiann kenji4569 DavidSchulz
  • Hidden Markov Model based fault diagnosis for stamping processes
    Mechanical Systems and Signal Processing, Vol. 18, No. 2. (March 2004), pp. 391-408.
    by M Ge, R Du, Y Xu
    posted to hmm diagnosis by ecome to the group LTN on 2007-04-05 08:59:08 as **
  • Detection and classification of abnormal process situations using multidimensional wavelet domain hidden Markov trees
    pp. 769-775.
    by A Bakhtazad
    posted to hmm by ecome to the group LTN on 2007-04-05 08:57:20 as **
  • A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests
    Systems, Man and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, Vol. 30, No. 4. (2000), pp. 463-473.
    by Jie Ying, T Kirubarajan, KR Pattipati, A Patterson-Hine
    posted to hmm by ecome to the group LTN on 2007-04-05 08:43:44 as **
  • The EM algorithm and Extension
    (1996)
    by J Mclachlan, T Krishnan
    posted to em by ecome to the group LTN on 2007-03-30 09:45:17 as **
  • Pattern Recognition and Machine Learning
    (2006)
    by C Bishop
    edited by M Jordan, J Kleinberg, B Sholkopf
    posted to no-tag by ecome to the group LTN on 2007-03-30 09:41:56 as **
  • Finite Mixture Models
    (2000)
    by Mc, D Peel
    posted to mixture em by ecome to the group LTN on 2007-03-30 09:39:14 as **
  • EM algorithm for partially known labels
    (2000), pp. 161-166.
    by C Ambroise, G Govaert
    edited by Springer
    posted to partial_labels em by ecome to the group LTN on 2007-03-30 09:23:18 as **
  • Handling possibilistic labels in pattern classification using evidential reasoning
    Fuzzy Sets and Systems, Vol. 122, No. 3. (2001), pp. 47-62.
    by T Denoeux, LM Zouhal
    posted to ds-theory by ecome to the group LTN on 2007-03-30 09:20:26 as **
  • Learning from an imprecise teacher: probabilistic and evidential approaches
    Vol. 1 (2001), pp. 100-105.
    by Ambroise
    posted to partial_labels em by ecome to the group LTN on 2007-03-30 09:18:14 as **
  • Logistic regression for partial labels
    Vol. III (2002), pp. 1935-1941.
    by Y Grandvallet
    posted to partial_labels logistic_regression by ecome to the group LTN on 2007-03-30 09:09:16 as **
  • Partially Supervised Learning by a Credal EM Approach
    : Symbolic and Quantitative Approaches to Reasoning with Uncertainty (2005), pp. 956-967.
    by Patrick Vannoorenberghe, Philippe Smets
    posted to ds-theory em by ecome to the group LTN on 2007-02-14 12:39:36 as read
  • Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem
    International Journal of Approximate Reasoning, Vol. 9, No. 1. (August 1993), pp. 1-35.
    by Philippe Smets
    posted to ds-theory gbt by ecome to the group LTN on 2007-02-14 12:38:41 as read
  • Belief functions on real numbers
    International Journal of Approximate Reasoning, Vol. 40, No. 3. (November 2005), pp. 181-223.
    by Philippe Smets
    posted to continuous ds-theory by ecome to the group LTN on 2007-02-14 12:36:56 as read
  • A Unified Approach to PCA, PLS, MLR and CCA
    by Magnus Borga, Tomas Landelius, Hans Knutsson
    posted to cca mlr pca pls by ecome to the group LTN on 2006-11-17 11:06:07 as read along with 1 person scotto36
  • Probabilités analyse des données et statistique
    (1990)
    by G Saporta
    edited by P Techni
    posted to general by ecome to the group LTN on 2006-09-25 14:04:18 as **
  • A maximum likelihood methodology for clusterwise linear regression
    Journal of Classification, Vol. 5 (1988), pp. 249-282.
    by Desarbo
    posted to clusterwise em by ecome to the group LTN on 2006-09-20 10:24:53 as **
  • ICA: A flexible non-linearity and decorrelating manifold approach
    Neural Computation, Vol. 11, No. 8. (1999), pp. 1957-1983.
    by Everson
    posted to ica by ecome to the group LTN on 2006-09-18 08:52:54 as **
  • Blind source separation with relative newton method
    (2003), pp. 897-902.
    by Michael Zibulevsky
    posted to ica newton optimization by ecome to the group LTN on 2006-08-28 12:35:22 as read
  • Propagation of probabilities, means and variances in Mixed graphical associations models
    Journal of the american statistical association, Vol. 87 (1992), pp. 1098-1108.
    by SL Lauritzen
    posted to gaussian gm linear mixed by ecome to the group LTN on 2006-08-28 09:26:07 as **
  • Imposing sparsity on the mixing matrix in independent component analysis
    Neurocomputing, Vol. 49 (2002), pp. 151-162.
    by Hyvärinen
    posted to ica prior sparse by ecome to the group LTN on 2006-08-28 09:12:14 as read
  • Independant Component Analysis
    (2001)
    by Aapo
    edited by Wiley Inter-Science
    posted to ica by ecome to the group LTN on 2006-08-28 09:06:01 as read
  • An Information-Maximization Approach to Blind Separation and Blind Deconvolution
    Neural Computation, Vol. 7, No. 6. (1995), pp. 1129-1159.
    by Anthony J Bell, Terrence J Sejnowski
    posted to ica by ecome to the group LTN on 2006-08-28 09:02:32 as ** along with 1 person jointhechink
  • An introduction to graphical models
    by M Jordan
    edited by UC Berkley
    posted to gm by ecome to the group LTN on 2006-08-25 11:10:01 as **
  • An introduction to MCMC for machine learning
    (2003)
    by C Andrieu, N de Freitas, A Doucet, M Jordan
    posted to mcmc by ecome to the group LTN on 2006-08-24 16:05:04 as ** along with 13 people ansobol mshafiei pool007 jvangael sugarexpletive Vezhnick lcigler Xavier vlachmore pkammerer tresch RhysU Hans45
  • Independent Factor Analysis
    Neural Computation, Vol. 11, No. 4. (1999), pp. 803-851.
    by Hagai Attias
    posted to em ica ifa by ecome to the group LTN on 2006-07-21 09:55:19 as **
  • Kernel ICA: An alternative formulation and its application to face recognition
    Pattern Recognition, Vol. 38, No. 10. (October 2005), pp. 1784-1787.
    by Jian Yang, Xiumei Gao, David Zhang, Jing-Yu Yang
    posted to ica kernel by ecome to the group LTN on 2006-07-21 09:42:31 as ** along with 1 person and 1 group garyfeng ReadingLab
  • A theory of Gaussian belief functions
    International Journal of Approximate Reasoning, Vol. 14, No. 2-3. ( 1996), pp. 95-126.
    by Liping Liu
    posted to gbf by ecome to the group LTN on 2006-07-18 14:49:15 as **
  • Local computation of Gaussian belief functions
    International Journal of Approximate Reasoning, Vol. 22, No. 3. (December 1999), pp. 217-248.
    by Liping Liu
    posted to gbf by ecome to the group LTN on 2006-07-18 14:48:37 as **
  • A Unifying Review of Linear Gaussian Models
    (1997)
    by Sam Roweis, Zoubin Ghahramani
    posted to em gm by ecome to the group LTN on 2006-06-26 12:30:25 as ** along with 10 people and 1 group noel wnpx tag sbarthelme dwf bamberg sugarexpletive daniel51 assad79 bhaddow metric_embeddings
  • Probabilistic principal component analysis
    (1997)
    by M Tipping, C Bishop
    posted to em pca by ecome to the group LTN on 2006-06-26 12:23:03 as ** along with 10 people and 2 groups pdlug davidr bpacker mad Theo walkking flavian_vasile ShaileshKumar vlachmore kohei-h vision-ng Computer_Graphics
  • EM Algorithms for PCA and SPCA
    Vol. 10 (1998)
    by Sam Roweis
    edited by Michael I Jordan, Michael J Kearns, Sara A Solla
    posted to em pca by ecome to the group LTN on 2006-06-26 09:57:01 as read along with 1 person and 4 groups jekky CMU-HCII Blog_and_Wiki_Research automatic_summarization ASR
  • Learning in Graphical Models (Adaptive Computation and Machine Learning)
    (1998)
    by M Jordan
    edited by Michael I Jordan
    posted to gm by ecome to the group LTN on 2006-06-19 11:07:04 as **
  • Dynamic Bayesian Networks: Representation, Inference and Learning
    (2002)
    by K Murphy
    edited by Uc
    posted to dbn hmm lds by ecome to the group LTN on 2006-06-19 10:47:41 as **
  • Un algorithme GEM pour le débruitage de signaux
    (2006)
    by Samé
    edited by Societée F de Classification
    posted to em regression by ecome to the group LTN on 2006-06-19 10:18:07 as **
  • Régression et modèle de mélange pour le débruitage de signaux
    (2006)
    by Côme
    posted to em regression by ecome to the group LTN on 2006-06-19 10:12:58 as **
  • Méthodes à noyaux pour la représentation et la discrimination de signaux non-stationnaires
    (2005)
    by Vincent Guigue
    posted to lars pursuit regression by ecome to the group LTN on 2006-06-08 09:57:16 as read
  • Kernel Matching Pursuit
    Mach. Learn., Vol. 48, No. 1-3. (2002), pp. 165-187.
    by Pascal Vincent, Yoshua Bengio
    posted to features-selections kernel pursuit regression by ecome to the group LTN on 2006-06-08 09:50:00 as **
  • Least angle regression
    Ann. Statist., Vol. 32, No. 2. (2004), pp. 407-499.
    by Bradley Efron, Trevor Hastie, Iain Johnstone, Robert Tibshirani
    posted to features-selections lars pursuit regression by ecome to the group LTN on 2006-06-07 09:38:56 as ** along with 1 person mukundn
  • A Note on the Decomposition Methods for Support Vector Regression
    by Shuo P Liao, Hsuan T Lin, Chih J Lin
    posted to kernel regression smo by ecome to the group LTN on 2006-06-02 10:26:50 as **
  • A tutorial on support vector regression
    (September 2003)
    by Smola
    posted to kernel regression sv by ecome to the group LTN on 2006-05-31 16:55:25 as read
  • Efficient SVM Regression Training with SMO
    Machine Learning (2001)
    by Gary W Flake, Steve Lawrence
    posted to constraints kernel quadratic regression smo by ecome to the group LTN on 2006-05-31 16:49:15 as **
  • An Incremental Multivariate Regression Method for Function Approximation from Noisy Data
    by Menita Carozza, Salvatore Rampone
    posted to constraints kernel optimization quadratic regression smo by ecome to the group LTN on 2006-05-31 16:48:27 as **
  • Improvements to the SMO Algorithm for SVM Regression
    IEEE-NN, Vol. 11, No. 5. (September 2000)
    by SK Shevade, SS Keerthi, C Bhattacharyya, KRK Murthy
    posted to constraints kernel optimization quadratic regression smo by ecome to the group LTN on 2006-05-31 16:47:59 as ** along with 1 person and 2 groups A_Olympia Philosophy_of_Information Blog_and_Wiki_Research
  • Sequential minimal optimization: A fast algorithm for training support vector machines
    (1998)
    by J Platt
    posted to constraints kernel optimization quadratic smo by ecome to the group LTN on 2006-05-31 16:47:03 as ** along with 3 people and 2 groups A_Olympia HlaingAungMin frcchang Philosophy_of_Information Blog_and_Wiki_Research
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