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markusd's machine-learning [109 articles]

当前文献位于 markusd's 文献库 标签分类为 machine-learning. You can also see everyone's machine-learning.
  • Hierarchical Dirichlet Processes
    Journal of the American Statistical Association, Vol. 101, No. 476. (December 2006), pp. 1566-1581.
    by Teh, Yee Whye, Jordan, I Michael, Beal, J Matthew, Blei, M David
    posted to machine-learning dirichlet by markusd on 2008-07-18 15:36:37 as ** along with 5 people bhaddow mshafiei ccr vlachmore asterix77
  • Fully Distributed EM for Very Large Datasets
    (2008)
    by Jason Wolfe, Aria Haghighi, Dan Klein
    posted to machine-learning em by markusd on 2008-07-18 15:36:16 as ** along with 1 person bhaddow
  • Modeling Online Reviews with Multi-Grain Topic Models
    (2008)
    by Ivan Titov, Ryan Mcdonald
    posted to topic-model machine-learning by markusd on 2008-07-18 15:34:28 as ** along with 1 person bhaddow
  • Accurate Max-margin Training for Structured Output Spaces.
    (2008)
    by Sunita Sarawagi, Rahul Gupta
    edited by Sam Roweis, Andrew Mccallum
    posted to machine-learning by markusd on 2008-07-18 15:33:30 as ** along with 1 person kedarb
  • Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo.
    (2008)
    by Salakhyuditnov, Mnih
    posted to machine-learning bayes by markusd on 2008-07-18 15:33:17 as ** along with 1 person kedarb
  • Learning Classifiers from Only Positive and Unlabeled Data
    (2008)
    by Charles Elkan, Keith Noto
    posted to negative-evidence machine-learning discriminative by markusd on 2008-07-18 15:32:55 as ** along with 1 person kedarb
  • An Algorithm to Determine Peer-Reviewers
    (24 May 2006)
    by Marko A Rodriguez, Johan Bollen
    posted to misc machine-learning by markusd on 2008-07-18 15:32:30 as ** along with 6 people and 1 group viktor21614 pak pdlug dullhunk nojhan JSicot Metaheuristics
  • The Dynamic Hierarchical Dirichlet Process
    (2008)
    by Lu Ren, David B Dunson, Lawrence Carin
    posted to machine-learning by markusd on 2008-07-18 15:32:13 as ** along with 1 person ldietz
  • Ultraconservative Online Algorithms for Multiclass Problems
    Vol. 2111 (2001), pp. 99-115.
    by Koby Crammer, Yoram Singer
    posted to mira machine-learning by markusd on 2008-07-17 18:35:58 as **
  • Nonparametric Bayesian Discrete Latent Variable Models for Unsupervised Learning
    (20 April 2007)
    by Dilan Görür
    posted to non-parametric machine-learning hidden-variable bayes by markusd on 2008-06-10 18:28:07 as ** along with 1 person ldietz
  • Grafting: fast, incremental feature selection by gradient descent in function space
    J. Mach. Learn. Res., Vol. 3 (2003), pp. 1333-1356.
    by Simon Perkins, Kevin Lacker, James Theiler
    posted to morphology-project machine-learning feature-selection crf by markusd on 2008-06-01 19:58:24 as ** along with 2 people and 1 group yaroslavvb bpacker vision-ng
  • Latent Dirichlet allocation
    (2002)
    by D Blei, A Ng, M Jordan
    posted to machine-learning by markusd on 2008-05-27 15:43:40 as ** along with 12 people and 4 groups pdlug jekky jeonghochang mshafiei llamerada viditjain anon_pl bayes bhaddow koji_matsuda ehohman cbosuna CMU-HCII Blog_and_Wiki_Research automatic_summarization ASR
  • Inducing Features of Random Fields
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 4. (1997), pp. 380-393.
    by Stephen Della Pietra, Vincent J Della Pietra, John D Lafferty
    posted to morphology-project machine-learning feature-selection crf by markusd on 2008-05-27 15:26:45 as **
  • Infinite Hidden Relational Models
    (2006)
    by Zhao Xu, Volker Tresp, Kai Yu, Hans-Peter Kriegel
    posted to machine-learning by markusd on 2008-05-13 16:12:38 as ** along with 1 person ldietz
  • Structured priors for structure learning
    (2006)
    by V Mansinghka, C Kemp, J Tenenbaum, T Griffiths
    posted to machine-learning by markusd on 2008-05-13 16:12:01 as ** along with 2 people roys ldietz
  • Hidden conditional random fields for phone classification
    (2005)
    by Asela Gunawardana, Milind Mahajan, Alex Acero, John C Platt
    posted to machine-learning hcrf discriminative crf by markusd on 2008-05-13 14:56:16 as **
  • Training algorithms for hidden conditional random fields
    (2006)
    by Milind Mahajan, Asela Gunawardana, Alex Acero
    posted to machine-learning hcrf discriminative crf by markusd on 2008-05-13 14:54:42 as **
  • Factor graphs and the sum-product algorithm
    Information Theory, IEEE Transactions on, Vol. 47, No. 2. (2001), pp. 498-519.
    by FR Kschischang, BJ Frey, HA Loeliger
    posted to morphology-project machine-learning by markusd on 2008-05-07 19:41:44 as ** along with 16 people tkollar XIANG nojhan dlmace yaroslavvb metamerist pcarbo marcosmuller rwm101 chenjinhui rsantana ElQuijote42 zeppe john_ardelius daniel51 delip
  • Inside-Outside Probability Computation for Belief Propagation
    (2007)
    by Taisuke Sato
    posted to machine-learning belief-propagation by markusd on 2008-05-07 19:41:33 as ** along with 1 person yaroslavvb
  • Walk-Sums and Belief Propagation in Gaussian Graphical Models
    Journal of Machine Learning Research, Vol. 7 (October 2006)
    by Dmitry Malioutov, Jason Johnson, Alan Willsky
    posted to machine-learning belief-propagation by markusd on 2008-05-07 19:38:16 as ** along with 2 people yaroslavvb daniel51
  • The Infinite Hidden Markov Model
    (2002)
    by MJ Beal, Z Ghahramani, CE Rasmussen
    posted to machine-learning by markusd on 2008-05-07 19:37:36 as ** along with 1 person nojhan
  • The Infinite Markov Model
    (2008)
    by Daichi Mochihashi, Eiichiro Sumita
    edited by JC Platt, D Koller, Y Singer, S Roweis
    posted to machine-learning by markusd on 2008-05-07 19:37:13 as ** along with 1 person whym
  • Bayesian Computation Via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods
    by AFM Smith, GO Roberts
    posted to machine-learning gibbs by markusd on 2008-05-07 19:34:04 as ** along with 3 people delip pcarbo icosma
  • PCA versus LDA
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2. (2001), pp. 228-233.
    by Aleix M Martinez, Avinash C Kak
    posted to machine-learning by markusd on 2008-05-07 17:01:11 as ** along with 1 person delip
  • PCA versus LDA
    IEEE Trans. Pattern Anal. Mach. Intell., Vol. 23, No. 2. (February 2001), pp. 228-233.
    by Aleix M Mart∈ez, Avinash C Kak
    posted to machine-learning by markusd on 2008-05-07 16:59:54 as ** along with 2 people bklynbam ldietz
  • Machine Learning
    (01 March 1997)
    by Tom M Mitchell
    posted to machine-learning by markusd on 2008-05-07 16:53:46 as ** along with 21 people and 3 groups bundschu pdlug yaroslavvb jelsas kmregan carueda jounih buescher76 mthomure hansjoergmueller brunomelo cerkut jvangael ajylha girschick zchen teesid dquercia robotact jdu sfeige AI RiSE contact-id
  • Exponential Priors for Maximum Entropy Models
    (2003)
    by J Goodman
    posted to machine-learning by markusd on 2008-05-07 16:51:53 as ** along with 2 people delip whym
  • The information bottleneck method
    (1999), pp. 368-377.
    by N Tishby, F Pereira, W Bialek
    posted to machine-learning by markusd on 2008-05-07 16:51:47 as ** along with 14 people and 4 groups dpf delip i-stevenson yaroslavvb geomblog gangli yiqingtu thsant mhay ajbattle sdvillal anon_pl 223rem vlachmore tulip kdl DataMining vision-ng
  • Pattern Recognition and Machine Learning (Information Science and Statistics)
    (28 August 2006)
    by Christopher M Bishop
    posted to sum-product machine-learning by markusd on 2008-05-07 16:51:39 as ** along with 24 people and 5 groups ssomani singhsh filippone quianominorleo matjes delip eddymier yaroslavvb sdvillal nojhan gkvas paulinus ajylha helsley hawkes zeppe poluxmoon librain kohei-h brianmc napvasconcelos teesid robotact Hoertnagl EvolutionaryComputation Metaheuristics Optimization contact-id MuSAMA
  • Information Theory, Inference & Learning Algorithms
    (15 June 2002)
    by David JC Mackay
    posted to machine-learning information-theory by markusd on 2008-05-07 16:51:28 as ** along with 39 people and 5 groups matjes delip lop ekatsyrk quianominorleo A_Olympia ansobol yaroslavvb JoSeK ingo jrw rpadams gyuli dariusb simonjudge leidner bayesian BarrosH wnpx filippone marcosmuller danielaugot zielaj daveh spirit_of_numenor sbarthelme anon_pl dwf petrijooste wowbagger nrubens chrisc rsantana fishtank rwilliamson poluxmoon robertoalamino brianmc robotact Philosophy_of_Information Blog_and_Wiki_Research AssistiveTechnology inference-group ComplexAdaptiveSystems
  • Discovering Significant Patterns
    Machine Learning
    by Geoffrey Webb
    posted to machine-learning by markusd on 2008-05-07 16:51:15 as ** along with 4 people briordan zoop katja msampson
  • Annealing stochastic approximation Monte Carlo algorithm for neural network training
    Machine Learning, Vol. 68, No. 3. (23 October 2007), pp. 201-233.
    by Faming Liang
    posted to machine-learning by markusd on 2008-05-07 16:51:05 as ** along with 1 person pcarbo
  • Discrete Mathematics for Computer Science, Some Notes
    (5 May 2008)
    by Jean Gallier
    posted to mathematics machine-learning by markusd on 2008-05-07 16:50:22 as ** along with 3 people jrw pdlug ansobol
  • What is principal component analysis?
    Nature Biotechnology, Vol. 26, No. 3., pp. 303-304.
    by Markus Ringnér
    posted to pca machine-learning by markusd on 2008-05-07 16:46:48 as ** along with 50 people and 3 groups rrbarb HugoMarcelo tharris rsantana kengg laharrin chrissp100 xingxu dekay23 rvosa i-stevenson dimka dayjm ashko craigwilson06 abbie_riner quianominorleo bibay2004 jfr kristgy pavanghatty rchauhan waghsk hpaces adacier fryanpan sobolevnrm Yanno jyuh operon barry baaic dullhunk Bevan_Weir zeji gareth massivemayhem zwang GustavoLacerda GeeSharpMinor bmaust zambujo mingzhi starz1010101 maralena wchannels sunhwan jennifermbui kdelavega zhouyu FAB-lab Bioinformatics baker-group
  • Probabilistic inference using Markov chain Monte Carlo methods
    (25 September 1993)
    by Radford M Neal
    posted to mcmc machine-learning by markusd on 2008-05-07 16:46:31 as ** along with 2 people delip ldietz
  • Getting Started in Probabilistic Graphical Models
    PLoS Computational Biology, Vol. 3, No. 12. (December 2007), pp. 2421-2425.
    by Edoardo M Airoldi
    posted to machine-learning by markusd on 2008-05-07 16:45:05 as ** along with 2 people delip ldietz
  • Markov Chain Monte Carlo in Practice
    (01 December 1995)
    by WR Gilks
    posted to mcmc machine-learning by markusd on 2008-05-07 16:44:57 as ** along with 12 people and 1 group przem abbie_riner ldietz fredguilloux rpadams eme Borelli metamerist wasteland93 lcigler Xavier roys astro
  • The Bayesian Choice
    (2007)
    by C Robert
    posted to machine-learning by markusd on 2008-05-07 16:43:18 as ** along with 1 person pcarbo
  • Metropolized independent sampling with comparisons to rejection sampling and importance sampling
    Statistics and Computing, Vol. 6, No. 2. (1 June 1996), pp. 113-119.
    by Jun S Liu
    posted to mcmc machine-learning gibbs by markusd on 2008-05-07 16:43:09 as ** along with 1 person pcarbo
  • Markov Chain Monte Carlo Method and Its Application
    by Stephen P Brooks
    posted to mcmc machine-learning gibbs by markusd on 2008-05-07 16:42:57 as ** along with 5 people delip microserf icosma alexv Xavier
  • Monte Carlo Sampling Methods Using Markov Chains and Their Applications
    Biometrika, Vol. 57, No. 1. (1970), pp. 97-109.
    by WK Hastings
    posted to mcmc machine-learning gibbs by markusd on 2008-05-07 16:42:48 as ** along with 4 people bfraser icosma rabio lcigler
  • Markov Chain Monte Carlo in Practice: A Roundtable Discussion
    The American Statistician, Vol. 52, No. 2. (1998), pp. 93-100.
    by Robert E Kass, Bradley P Carlin, Andrew Gelman, Radford M Neal
    posted to mcmc machine-learning gibbs by markusd on 2008-05-07 16:42:39 as ** along with 4 people jrblevin i-stevenson ddahlem icosma
  • Facilitating the Gibbs Sampler: The Gibbs Stopper and the Griddy-Gibbs Sampler
    Journal of the American Statistical Association, Vol. 87, No. 419. (1992), pp. 861-868.
    by Christian Ritter, Martin A Tanner
    posted to mcmc machine-learning gibbs by markusd on 2008-05-07 16:42:24 as ** along with 1 person icosma
  • Bayesian Statistics without Tears: A Sampling-Resampling Perspective
    The American Statistician, Vol. 46, No. 2. (1992), pp. 84-88.
    by AFM Smith, AE Gelfand
    posted to mcmc machine-learning gibbs by markusd on 2008-05-07 16:42:12 as ** along with 3 people frohike abbie_riner icosma
  • The Calculation of Posterior Distributions by Data Augmentation
    Journal of the American Statistical Association, Vol. 82, No. 398. (1987), pp. 528-540.
    by Martin A Tanner, Wing H Wong
    posted to machine-learning by markusd on 2008-05-07 16:41:56 as ** along with 4 people and 1 group abbie_riner icosma plm rharang SAA-HSA
  • Hierarchical structure and the prediction of missing links in networks
    Nature, Vol. 453, No. 7191., pp. 98-101.
    by Aaron Clauset, Cristopher Moore, MEJ Newman
    posted to machine-learning by markusd on 2008-05-07 16:36:05 as ** along with 35 people sen_cheng jjray rds Borelli ptrobajo apwesolowski dpf cmmorel fbaroni balicea cmor kengg pbull jago Ema gtgt15 hryk nedwards i-stevenson pinney elsantosneto eyliu craigwilson06 jyuh margaritis gagliol bryanhughes akshat tmmurali cactus AbnerCYH tny briordan deanmalmgren soramame_0518
  • Understanding the Metropolis-Hastings Algorithm
    The American Statistician, Vol. 49, No. 4. (1995), pp. 327-335.
    by Siddhartha Chib, Edward Greenberg
    posted to machine-learning by markusd on 2008-05-07 16:30:50 as ** along with 11 people and 2 groups i-stevenson ansobol quianominorleo jrw rvosa nojhan ldietz pcarbo icosma adriandefroment leonnovelo FAB-lab Metaheuristics
  • Learning Dynamic Bayesian Networks
    Lecture Notes in Computer Science, Vol. 1387 (1998), pp. 168-197.
    by Zoubin Ghahramani
    posted to machine-learning by markusd on 2008-05-06 20:33:21 as ** along with 3 people bruno dcoates pthimon
  • Accelerated training of conditional random fields with stochastic gradient methods
    (2006)
    by Nicol N Vishwanathan
    posted to machine-learning by markusd on 2008-05-01 00:23:49 as ** along with 1 person whym
  • Composition of Conditional Random Fields for Transfer Learning
    (October 2005), pp. 748-754.
    by Charles Sutton, Andrew Mccallum
    posted to machine-learning by markusd on 2008-05-01 00:21:55 as **
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