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タグ: machine-learning [544 articles]

Recent papers classified by the tag machine-learning.
  • Discovering Significant Patterns
    Machine Learning
    by Geoffrey Webb
    posted to machine-learning by zoop on 2007-04-23 15:20:47 as ** along with 4 people markusd briordan katja msampson
  • Information Theory, Inference & Learning Algorithms
    (15 June 2002)
    by David JC Mackay
    posted to codes machine-learning neural-networks by zielaj on 2006-03-13 19:43:09 as ** along with 40 people and 5 groups luispedro matjes delip lop ekatsyrk quianominorleo markusd A_Olympia ansobol yaroslavvb JoSeK ingo jrw rpadams gyuli dariusb simonjudge leidner bayesian BarrosH wnpx filippone marcosmuller danielaugot 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
  • Integrating probabilistic extraction models and data mining to discover relations and patterns in text
    (2006), pp. 296-303.
    by Aron Culotta, Andrew Mccallum, Jonathan Betz
    posted to machine-learning text-mining by yas on 2008-01-08 22:05:35 as ** along with 2 people and 2 groups AlisonBabeu dejori ic4 generif_paper
  • Machine learning in bioinformatics.
    Brief Bioinform, Vol. 7, No. 1. (March 2006), pp. 86-112.
    by P Larrañaga, B Calvo, R Santana, C Bielza, J Galdiano, I Inza, JA Lozano, R Armañanzas, G Santafé, A Pérez, V Robles
    posted to machine-learning review by yas on 2006-06-11 07:18:31 as ** along with 5 people and 2 groups jfr caporaso rsantana dimatura xili03 T_lab biomedical-nlp
  • notes General conditions for predictivity in learning theory.
    Nature, Vol. 428, No. 6981. (25 March 2004), pp. 419-422.
    by T Poggio, R Rifkin, S Mukherjee, P Niyogi
    posted to colt machine-learning by yaroslavvb on 2005-02-23 05:00:12 as ** along with 5 people and 1 group klouie kentsis davidr sdvillal kaineci Glimcher_Lab
  • notes Value Regularization and Fenchel Duality
    J. Mach. Learn. Res., Vol. 8 (2007), pp. 441-479.
    by Ryan M Rifkin, Ross A Lippert
    posted to kernel machine-learning regularization by yaroslavvb on 2008-01-29 07:06:14 as ** along with 1 person and 1 group mdreid Statistical Machine Learning
  • notes Neural Networks for Pattern Recognition
    (01 November 1995)
    by Christopher M Bishop
    posted to book machine-learning by yaroslavvb on 2007-12-18 22:44:55 as ** along with 13 people and 5 groups fredguilloux eme leidner jekky buescher76 arielbaz filippone ephel Found poluxmoon brianmc aklassen olbudt CMU-HCII Blog_and_Wiki_Research automatic_summarization ASR astro
  • notes The information bottleneck method
    (1999), pp. 368-377.
    by N Tishby, F Pereira, W Bialek
    posted to information-theory machine-learning by yaroslavvb on 2005-03-05 00:28:32 as read along with 14 people and 4 groups dpf delip i-stevenson markusd geomblog gangli yiqingtu thsant mhay ajbattle sdvillal anon_pl 223rem vlachmore tulip kdl DataMining vision-ng
  • notes Pattern Recognition and Machine Learning (Information Science and Statistics)
    (28 August 2006)
    by Christopher M Bishop
    posted to book linear-algebra machine-learning by yaroslavvb on 2007-08-30 06:52:34 as ** along with 24 people and 5 groups ssomani singhsh filippone quianominorleo matjes delip markusd eddymier sdvillal nojhan gkvas paulinus ajylha helsley hawkes zeppe poluxmoon librain kohei-h brianmc napvasconcelos teesid robotact Hoertnagl EvolutionaryComputation Metaheuristics Optimization contact-id MuSAMA
  • Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms
    (2002), pp. 1-8.
    by Michael Collins
    posted to hidden-markov-models machine-learning perceptron by whym on 2007-07-16 17:32:03 as ***
  • CRF-Filters: Discriminative Particle Filters for Sequential State Estimation
    Robotics and Automation, 2007 IEEE International Conference on (2007), pp. 3142-3147.
    by B Limketkai, D Fox, Lin Liao
    posted to machine-learning by whym on 2008-05-22 06:19:38 as **
  • Efficient Training of Conditional Random Fields
    (2002)
    by Hanna Wallach
    posted to dynamic-programming graphical-models machine-learning sequence-labeling by whym on 2007-04-22 13:56:14 as ****
  • Exponential Priors for Maximum Entropy Models
    (2003)
    by J Goodman
    posted to bayesian machine-learning maximum-entropy by whym on 2007-11-29 06:53:19 as *** along with 2 people delip markusd
  • Random projection in dimensionality reduction: applications to image and text data
    (2001), pp. 245-250.
    by Ella Bingham, Heikki Mannila
    posted to machine-learning by whym on 2008-05-08 11:01:02 as ** along with 2 people jelsas yama_tah
  • A comparison of algorithms for maximum entropy parameter estimation
    (2002), pp. 1-7.
    by Robert Malouf
    posted to machine-learning maximum-entropy optimization by whym on 2007-05-29 01:04:31 as ** along with 2 people erikv bhaddow
  • Maximum Entropy Markov Models for Information Extraction and Segmentation
    (2000), pp. 591-598.
    by Andrew Mccallum, Dayne Freitag, Fernando Pereira
    posted to machine-learning maximum-entropy sequence-labeling by whym on 2007-05-29 09:38:33 as ** along with 6 people halacsy unnonouno buescher76 lyongu syoyo Kazuki
  • Scaling to very very large corpora for natural language disambiguation
    (2001), pp. 26-33.
    by Michele Banko, Eric Brill
    posted to active-learning machine-learning spelling-correction by whym on 2007-12-22 15:42:18 as ** along with 2 people kawatan vlachmore
  • Latent-Dynamic Discriminative Models for Continuous Gesture Recognition
    Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on (2007), pp. 1-8.
    by Louis-Philippe Morency, Ariadna Quattoni, Trevor Darrell
    posted to machine-learning sequence-labeling by whym on 2007-12-13 08:26:21 as **
  • Strong Markov Random Field Model
    by Rupert Paget
    posted to graphical-models machine-learning maximum-entropy by whym on 2007-05-29 23:58:20 as **
  • Semi-Markov Conditional Random Fields for Information Extraction
    by Sunita Sarawagi, William W Cohen
    posted to graphical-models machine-learning segmentation sequence-labeling by whym on 2007-07-11 13:53:50 as ** along with 3 people pdlug davidr iris_2001
  • A sampling-based approach to nonparametric dynamic system identification and estimation
    American Control Conference, 2004. Proceedings of the 2004, Vol. 1 (2004), pp. 873-879 vol.1.
    by Songhwai Oh, Jin Kim, S Sastry
    posted to ai dynamical-systems machine-learning prameters by watson on 2008-02-09 12:22:02 as **
  • Geometry from a Time Series
    Physical Review Letters, Vol. 45, No. 9. (1980), 712.
    by NH Packard, JP Crutchfield, JD Farmer, RS Shaw
    posted to patterns methods machine-learning dynamical-systems analysis by watson on 2008-05-14 06:58:52 as ****
  • The bifurcating neuron network 2: an analog associative memory
    Neural Netw., Vol. 15, No. 1. (January 2002), pp. 69-84.
    by Geehyuk Lee, Nabil H Farhat
    posted to neural networks machine-learning integrate-and-fire dynamics bifurcation associative-memory by watson on 2008-05-14 05:44:02 as **** along with 1 person and 1 group dcoates NeuralNetworks
  • The Mathematics of Learning: Dealing with Data
    Neural Networks and Brain, 2005. ICNN&B '05. International Conference on, Vol. 1 (2005), pp. PL-5-PL-23.
    by T Poggio, S Smale
    posted to machine-learning by vaasu on 2006-05-20 16:59:17 as ** along with 3 people and 2 groups craigtalbert bamberg thorgal Netmob P2P
  • Reinforcement Learning: A Survey
    Journal of Artificial Intelligence Research, Vol. 4 (1996), pp. 237-285.
    by Leslie P Kaelbling, Michael L Littman, Andrew P Moore
    posted to machine-learning by vaasu on 2006-05-20 16:25:25 as ** along with 20 people and 5 groups Tominator KurtSteinkraus eldan unnonouno dep cyph3r davidmihalcik craigtalbert damcci hllee lop afoerster gagliol sugarexpletive mzygmunt dowlingj josepe docB bach tdahl Glimcher_Lab Netmob P2P SensorNetworks kickOffTUG-Robocup
  • Lazy learning for local modeling and control design
    (1997)
    by B Birattari, M Bersini
    posted to machine-learning by vaasu on 2006-05-20 15:47:02 as ** along with 2 groups Netmob P2P
  • Efficient Memory-based Learning for Robot Control
    (1990)
    by AW Moore
    posted to machine-learning by vaasu on 2006-05-20 15:26:14 as ** along with 2 groups Netmob P2P
  • Sample complexity of model-based search
    (1998), pp. 259-267.
    by Christopher D Rosin
    posted to machine-learning by vaasu on 2006-05-20 15:17:09 as ** along with 2 groups Netmob P2P
  • Local search with constraint propagation and conflict-based heuristics
    Artif. Intell., Vol. 139, No. 1. (July 2002), pp. 21-45.
    by Narendra Jussien, Olivier Lhomme
    posted to machine-learning by vaasu on 2006-05-20 15:14:23 as ** along with 1 person and 2 groups craigtalbert Netmob P2P
  • Introduction to Statistical Pattern Recognition, Second Edition (Computer Science and Scientific Computing Series)
    (28 September 1990)
    by Keinosuke Fukunaga
    posted to machine-learning by teesid on 2007-12-18 11:36:53 as ** along with 3 people matjes yaroslavvb sdvillal
  • The Nature of Statistical Learning Theory (Information Science and Statistics)
    (19 November 1999)
    by Vladimir N Vapnik
    posted to machine-learning by teesid on 2007-12-18 10:39:59 as *** along with 12 people akastrin jaonary ecome yaroslavvb creswick mthomure sdvillal atbrew girschick nmdang navil jung_dalglish
  • Machine Learning
    (01 March 1997)
    by Tom M Mitchell
    posted to machine-learning by teesid on 2007-12-18 10:38:44 as read along with 21 people and 3 groups bundschu markusd pdlug yaroslavvb jelsas kmregan carueda jounih buescher76 mthomure hansjoergmueller brunomelo cerkut jvangael ajylha girschick zchen dquercia robotact jdu sfeige AI RiSE contact-id
  • Nearest neighbor pattern classification
    Information Theory, IEEE Transactions on, Vol. 13, No. 1. (1967), pp. 21-27.
    by T Cover, P Hart
    posted to machine-learning by teesid on 2007-12-19 12:00:37 as ***** along with 2 people atbrew sampath
  • Maximal margin classification for metric spaces
    J. Comput. Syst. Sci., Vol. 71, No. 3. (October 2005), pp. 333-359.
    by Matthias Hein, Olivier Bousquet, Bernhard Schölkopf
    posted to machine-learning by teesid on 2007-12-20 07:54:11 as ****
  • Rank, Trace-Norm and Max-Norm
    Learning Theory (2005), pp. 545-560.
    by Nathan Srebro, Adi Shraibman
    posted to machine-learning by teesid on 2008-02-07 09:47:31 as *****
  • Think globally, fit locally: unsupervised learning of low dimensional manifolds
    J. Mach. Learn. Res., Vol. 4 (2003), pp. 119-155.
    by Lawrence K Saul, Sam T Roweis
    posted to locally-linear-embedding machine-learning by tag on 2006-01-13 03:39:31 as ** along with 3 people and 2 groups yaroslavvb gangli sdvillal tulip metric_embeddings
  • A Unifying Review of Linear Gaussian Models
    (1997)
    by Sam Roweis, Zoubin Ghahramani
    posted to machine-learning by tag on 2006-01-13 03:29:31 as ** along with 9 people and 2 groups noel wnpx ecome sbarthelme dwf bamberg sugarexpletive daniel51 assad79 metric_embeddings LTN
  • notes Nonlinear Dimensionality Reduction by Locally Linear Embedding
    Science, Vol. 290, No. 5500. (22 December 2000), pp. 2323-2326.
    by Sam T Roweis, Lawrence K Saul
    posted to locally-linear-embedding machine-learning by tag on 2006-01-13 02:31:43 as ** along with 15 people and 4 groups matjes amargoli yaroslavvb gangli yas yama_tah filippone sdvillal dgront walkking jguinney kenji4569 DavidSchulz mdramirez mctor tulip T_lab metric_embeddings Computer_Graphics
  • notes A global geometric framework for nonlinear dimensionality reduction.
    Science, Vol. 290, No. 5500. (22 December 2000), pp. 2319-2323.
    by JB Tenenbaum, V de Silva, JC Langford
    posted to locally-linear-embedding machine-learning by tag on 2006-01-13 02:25:29 as ** along with 15 people and 4 groups matjes aufrank k12u yas metamerist yama_tah mthomure sdvillal dgront voiklis dwf DavidSchulz teesid jung_dalglish mctor ur-cls T_lab metric_embeddings ColDyn
  • Gene Selection for Cancer Classification using Support Vector Machines
    Mach. Learn., Vol. 46, No. 1-3. (2002), pp. 389-422.
    by Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik
    posted to feature-ranking feature-selection linear machine-learning supervised svm by sterovetta on 2008-01-16 15:39:05 as read along with 2 people sdvillal mmunson
  • K-winner machines for pattern classification
    Neural Networks, IEEE Transactions on, Vol. 12, No. 2. (2001), pp. 371-385.
    by S Ridella, S Rovetta, R Zunino
    posted to classification k-winner-machine machine-learning transductive vector-quantization by sterovetta on 2007-11-15 12:49:03 as ** along with 1 person dimatura
  • Learning Computational Grammars
    (15 Jul 2001)
    by John Nerbonne, Anja Belz, Nicola Cancedda, Herve Dejean, James Hammerton, Rob Koeling, Stasinos Konstantopoulos, Miles Osborne, Franck Thollard, Erik
    posted to machine-learning nlp by stasinos on 2006-10-03 14:18:27 as read
  • Exploiting time-varying relationships in statistical relational models
    (2007), pp. 9-15.
    by Umang Sharan, Jennifer Neville
    posted to classification data-mining machine-learning related temporal by ssn on 2008-03-18 11:52:43 as read along with 1 person rsantana
  • Filtering for personal web information agents
    (2004), pp. 588-589.
    by Gabriel L Somlo, Adele E Howe
    posted to information-filtering information-retrieval machine-learning poster web by ssn on 2006-03-24 16:16:56 as read
  • Applying web analysis in web page filtering
    (2004), pp. 376-376.
    by Michael Chau
    posted to information-retrieval machine-learning poster by ssn on 2006-03-24 16:11:42 as read
  • Implementation and evaluation of a quality-based search engine
    (2006), pp. 73-84.
    by Thomas Mandl
    posted to evaluation machine-learning prodei web webir by ssn on 2007-02-15 15:42:32 as read along with 1 person creswick
  • An Introduction to Computational Learning Theory
    (15 August 1994)
    by Michael J Kearns, Umesh V Vazirani
    posted to computer-science machine-learning statistics by spirit_of_numenor on 2006-04-30 20:12:47 as **** along with 3 people and 1 group dsilva BarrosH AbnerCYH ComplexAdaptiveSystems
  • An overview of statistical learning theory
    Neural Networks, IEEE Transactions on, Vol. 10, No. 5. (1999), pp. 988-999.
    by VN Vapnik
    posted to machine-learning by sourada on 2005-10-18 04:20:07 as ** along with 16 people and 9 groups fredguilloux A_Olympia yaroslavvb anoopsarkar gangli eme garyfeng arsyed yish sdvillal gane5h dwf atbrew d_burfoot rsantana balduzzi Philosophy_of_Information Blog_and_Wiki_Research tulip ReadingLab astro mathgamespatterns dtl vds-arg eni
  • Introduction to Machine Learning (Adaptive Computation and Machine Learning)
    (01 October 2004)
    by Ethem Alpaydin
    posted to machine-learning by smallbadguy on 2007-02-01 14:40:26 as ** along with 7 people and 3 groups balicea quianominorleo jliegl pdlug sb-3000 ryanshaw rhino158 18th-century_history_culture_and_society sims_phd_cohort_2005 digital_youth
  • PubMiner: Machine Learning-Based Text Mining System for Biomedical Information Mining
    Lecture Notes in Computer Science : Artificial Intelligence: Methodology, Systems, and Applications (2004), pp. 216-225.
    by Jae-Hong Eom, Byoung-Tak Zhang
    posted to machine-learning natural-language-processing text-mining by simmie on 2006-12-01 13:04:08 as read
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