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  • Tamás SZIRÁNYI
    Publications: http://www.sztaki.hu/~sziranyi/publ_szt.html 
     

  • Ioannis Pitas
    http://poseidon.csd.auth.gr/LAB_PEOPLE/IPitas.htm  
     

  • Joe Noonan
    http://www.eecs.tufts.edu/~jnoonan/  
     

  • ACIL Publications
    http://www.ece.umr.edu/acil/publications.htm 
     

  • Tobias Sing 
    http://www.tobiassing.net 
     

  • Avishai Adler
     

  •  
    http://csl.snu.ac.kr/publication/paper/dissertation/Ph.D/nojunkwak.pdf  

     

  • Publications 
    http://csl.snu.ac.kr/ 
     

  • NEW VISL PROJECT PROPOSALS FOR SPRING TERM 2005
    http://visl.technion.ac.il/projects_spring_2005.htm  
     

  • The Equations Behind the Brain
    http://visl.technion.ac.il/projects/2005spring/karigal4.pdf  
     

  • Larisa Goffman-Vinopal
    http://visl.technion.ac.il/~lg/ 
     

  • Andri Riid
    Publications http://www.fmt.vein.hu/softcomp/publications.html  
     

  • Thesis PFE
    http://www.antoineazar.com/PFE.pdf  
     

  • Miguel Ángel González Ballester
    Publications: http://www-sop.inria.fr/epidaure/BIBLIO/Author/GONZALEZ-BALLESTER-MA.html  
     

  • Alex Zelikovsky
    Publications: http://suez.cs.gsu.edu/%7Ecscazz/publications.html  
     

  • Towards a computer-aided diagnosis system for ... pigmented skin lesions 
     

  • Papers that cite Woods, Kegelmeyer, and Bowyer
    IEEE Trans PAMI, April 1997.
     

  • Shin HW, Sohn SY
    Selected tree classifier combination based on both accuracy and error diversity 
    PATTERN RECOGNITION 38 (2): 191-197 FEB 2005 
     

  • Myles AJ, Brown SD
    Decision pathway modeling 
    JOURNAL OF CHEMOMETRICS 18 (6): 286-293 JUN 2004 
     

  • Tsoumakas G, Katakis I, Vlahavas I
    Effective voting of heterogeneous classifiers 
    LECTURE NOTES IN COMPUTER SCIENCE 3201: 465-476 2004 
     

  • McDonald RA, Eckley IA, Hand DJ
    A classifier combination tree algorithm 
    LECTURE NOTES IN COMPUTER SCIENCE 3138: 609-617 2004 
     

  • Rohlfing T, Russakoff DB, Maurer CR
    Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation 
    IEEE TRANSACTIONS ON MEDICAL IMAGING 23 (8): 983-994 AUG 2004 

  • Zhang Z, Zhou SG, Zhou AY
    Sequential classifiers combination for text categorization: An experimental study 
    LECTURE NOTES IN COMPUTER SCIENCE 3129: 509-518 2004 
     

  • Dynamic classifier selection. by adaptive k-nearest-neighbourhood rule 
    Didaci L, Giacinto G
    LECTURE NOTES IN COMPUTER SCIENCE 3077: 174-183 2004 
     

  • Ensembles of learning machines
    Valentini G, Masulli R
    LECT NOTES COMPUT SC 2486: 3-19 2002 
     

  • "Fuzzy" versus "Nonfuzzy" in combining classifiers designed by boosting
    Kuncheva LI
    IEEE T FUZZY SYST 11 (6): 729-741 DEC 2003 
     

  • A credit assignment approach to fusing classifiers of multiseason hyperspectral imagery
    Bachmann CM, Bettenhausen MH, Fusina RA, et al.
    IEEE T GEOSCI REMOTE 41 (11): 2488-2499 Part 1 NOV 2003 
     

  • UODV: improved algorithm and generalized theory
    Jing XY, Zhang D, Jin Z
    PATTERN RECOGN 36 (11): 2593-2602 NOV 2003 
     

  • Liu WY, Song N
    A fuzzy approach to classification of text documents
    J COMPUT SCI TECHNOL 18 (5): 640-647 SEP 2003 

    Maloof MA, Langley P, Binford TO, et al.
    Improved rooftop detection in aerial images with machine learning
    MACH LEARN 53 (1-2): 157-191 OCT-NOV 2003 

    Kamel MS, Wanas NM
    Data dependence in combining classifiers
    LECT NOTES COMPUT SC 2709: 1-14 2003
     

  • A sequential scheduling approach to combining multiple object classifiers using cross-entropy
    Magee D
    LECT NOTES COMPUT SC 2709: 135-145 2003
     

  • The practical performance characteristics of tomographically filtered multiple classifier fusion
    Windridge D, Kittler J
    LECT NOTES COMPUT SC 2709: 186-195 2003
     

  • Improvements on the uncorrelated optimal discriminant vectors
    Jing XY, Zhang D, Jin Z
    PATTERN RECOGN 36 (8): 1921-1923 AUG 2003
     

  • Combining both ensemble and dynamic classifier selection schemes for prediction of mobile internet subscribers
    Shin HW, Sohn SY
    EXPERT SYST APPL 25 (1): 63-68 JUL 2003
     

  • Altincay H, Demirekler M
    Undesirable effects of output normalization in multiple classifier systems
    PATTERN RECOGN LETT 24 (9-10): 1163-1170 JUN 2003
     

  • A morphologically optimal strategy for classifier combination: Multiple expert fusion as a tomographic process
    Windridge D, Kittler J
    IEEE T PATTERN ANAL 25 (3): 343-353 MAR 2003

    Oh SB
    On the relationship between majority vote accuracy and dependency in multiple classifier systems
    PATTERN RECOGN LETT 24 (1-3): 359-363 JAN 2003

    Ghosh J
    Multiclassifier systems: Back to the future
    LECT NOTES COMPUT SC 2364: 1-15 2002
     

  • On the general application of the tomographic classifier fusion methodology
    Windridge D, Kittler J
    LECT NOTES COMPUT SC 2364: 149-158 2002
     

  • Post-processing of classifier outputs in multiple classifier systems
    Altincay H, Demirekler M
    LECT NOTES COMPUT SC 2364: 159-168 2002
     

  • A combination scheme for fuzzy clustering
    Dimitriadou E, Weingessel A, Hornik K
    INT J PATTERN RECOGN 16 (7): 901-912 NOV 2002
     

  • A modular clutter rejection technique for FLIR imagery using region-based principal component analysis
    Rizvi SA, Nasrabadi NM
    PATTERN RECOGN 35 (12): 2895-2904 DEC 2002
     

  • Alkoot FM, Kittler J
    Moderating k-NN classifiers
    PATTERN ANAL APPL 5 (3): 326-332 JUN 2002
     

  • Plurality voting-based multiple classifier systems: statistically independent with respect to dependent classifier sets
    Demirekler M, Altincay H
    PATTERN RECOGN 35 (11): 2365-2379 NOV 2002
     

  • Fusing neural networks through space partitioning and fuzzy integration
    Verikas A, Lipnickas A
    NEURAL PROCESS LETT 16 (1): 53-65 AUG 2002
     

  • Modeling focus of attention for meeting indexing based on multiple cues
    Stiefelhagen R, Yang J, Waibel A
    IEEE T NEURAL NETWOR 13 (4): 928-938 JUL 2002
     

  • A data complexity analysis of comparative advantages of decision forest constructors
    Ho TK
    PATTERN ANAL APPL 5 (2): 102-112 JUN 2002
     

  • Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection
    Smits PC
    IEEE T GEOSCI REMOTE 40 (4): 801-813 APR 2002
     

  • Modified product fusion
    Alkoot FM, Kittler J
    PATTERN RECOGN LETT 23 (8): 957-965 JUN 2002
     

  • Switching between selection and fusion in combining classifiers: An experiment
    Kuncheva LI
    IEEE T SYST MAN CY B 32 (2): 146-156 APR 2002
     

  • Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery
    Yu SX, De Backer S, Scheunders P
    PATTERN RECOGN LETT 23 (1-3): 183-190 JAN 2002
     

  • Selection of classifiers based on multiple classifier behaviour
    Giacinto G, Roli F, Fumera G
    LECT NOTES COMPUT SC 1876: 87-93 2000
     

  • Windridge D, Kittler J
    Combined classifier optimisation via feature selection
    LECT NOTES COMPUT SC 1876: 687-695 2000

    Zhu H, Beling PA, Overstreet G
    A study in the combination of two consumer credit scores
    J OPER RES SOC 52 (9): 974-980 SEP 2001

    Lashkia V, Aleshin S
    Test feature classifiers: Performance and applications
    IEEE T SYST MAN CY B 31 (4): 643-650 AUG 2001

    Rao NSV
    On fusers that perform better than best sensor
    IEEE T PATTERN ANAL 23 (8): 904-909 AUG 2001

    Kuncheva LI
    Using measures of similarity and inclusion for multiple classifier
    fusion by decision templates
    FUZZY SET SYST 122 (3): 401-407 SEP 16 2001

    Giacinto G, Roli F
    Dynamic classifier selection based on multiple classifier behaviour
    PATTERN RECOGN 34 (9): 1879-1881 SEP 2001
     

  • Jin Z, Yang JY, Hu ZS, et al.
    Face recognition based on the uncorrelated discriminant transformation
    PATTERN RECOGN 34 (7): 1405-1416 JUL 2001
     

  • Valet L, Mauris G, Bolon P
    A statistical overview of recent literature in information fusion
    IEEE AERO EL SYS MAG 16 (3): 7-14 MAR 2001
     

  • Kuncheva LI, Bezdek JC, Duin RPW
    Decision templates for multiple classifier fusion: an experimental comparison
    PATTERN RECOGN 34 (2): 299-314 FEB 2001
     

  • Kuncheva LI, Jain LC
    Designing classifier fusion systems by genetic algorithms
    IEEE T EVOLUT COMPUT 4 (4): 327-336 NOV 2000

    Rizvi SA, Saadawi TN, Nasrabadi NM
    A clutter rejection technique for FLIR imagery using region based
    principal component analysis
    PATTERN RECOGN 33 (11): 1931-1933 NOV 2000

    Happel MD, Bock P
    Analysis of a fusion method for combining marginal classifiers
    LECT NOTES COMPUT SC 1857: 137-146 2000
     

  • Dynamic classifier selection
    Giacinto G, Roli F
    LECT NOTES COMPUT SC 1857: 177-189 2000
     

  • Filterbank-based fingerprint matching
    Jain AK, Prabhakar S, Hong L, et al.
    IEEE T IMAGE PROCESS 9: (5) 846-859 MAY 2000.
     

  • Distance-based test feature classifiers and its applications
    Lashkia V, Kaneko S, Aleshin S
    IEICE T INF SYST E83D: (4) 904-913 APR 2000.
     

  • Selection of image classifiers
    Giacinto G, Roli F, Fumera G
    ELECTRON LETT 36: (5) 420-422 MAR 2 2000.
     

  • Statistical pattern recognition: A review
    Jain AK, Duin RPW, Mao JC
    IEEE T PATTERN ANAL 22: (1) 4-37 JAN 2000.
     

  • Neural networks based colour measuring for process monitoring and control in multicoloured newspaper printing
    Verikas A, Malmqvist K, Bergman L
    NEURAL COMPUT APPL 9 (3): 227-242 2000
     

  • Experimental evaluation of expert fusion strategies
    Alkoot FM, Kittler J
    PATTERN RECOGN LETT 20: (11-13) 1361-1369 NOV 1999.
     

  • Miller DJ, Yan L
    Critic-driven ensemble classification
    IEEE T SIGNAL PROCES 47: (10) 2833-2844 OCT 1999.

    Li YH, Jain AK
    Classification of text documents
    COMPUT J 41: (8) 537-546 1998.

    Verikas A, Malmqvist K, Malmqvist L, et al.
    A new method for colour measurements in graphic arts
    COLOR RES APPL 24: (3) 185-196 JUN 1999.

    Verikas A, Lipnickas A, Malmqvist K, et al.
    Soft combination of neural classifiers: A comparative study
    PATTERN RECOGN LETT 20: (4) 429-444 APR 1999.
     

  • A multichannel approach to fingerprint classification
    Jain AK, Prabhakar S, Hong L
    IEEE T PATTERN ANAL 21: (4) 348-359 APR 1999.
     

  • Application of multi-layer perceptron neural networks to vision problems
    Khotanzad A, Chung C
    NEURAL COMPUT APPL 7: (3) 249-259 1998.
     

  • Colour classification by neural networks in graphic arts
    Verikas A, Malmqvist K, Bergman L, et al.
    NEURAL COMPUT APPL 7: (1) 52-64 1998.
     

  • Wang LC, Der SZ, Nasrabadi NM
    Composite classifiers for automatic target recognition
    OPT ENG 37: (3) 858-868 MAR 1998. 
     

  • Loop Recognition
    http://www.mssl.ucl.ac.uk/twiki/bin/view/SDO/LoopRecognition 
     

  • Need to get this guy's journal papers ...
    http://www.egr.msu.edu/~aviyente/  
     

  • Papers
    http://sclab.yonsei.ac.kr/~uribyul/ic.htm 
     

  •  
    http://tswww.ism.ac.jp/higuchi/index_e/bib_r.html 
     
  •  
    http://www.ism.ac.jp/editsec/resmemo/resmemo-file/resm-2e.htm  
     
  •  
    http://www.ism.ac.jp/publish/index_e.html  
     
  • Parminder Nagra 
     
  • P303.html @ vintage synthony.com 
     
  • Octasic OCT61000  echo cancellation chip 
     
  • SanteraOne by Tekelec 
     

New 26-07-2004

New 12-07-2004

New

New 03-05-2005

New 25-04-2005

 

12-04-2005

21-03-2005

New 17-03-2005

New 10-01-2005

New 12-12-2004

  • Jan Even Øie Nilsen
    http://www.nersc.no/~even/ 
     
  •  
    http://www.ai.univie.ac.at/cgi-bin/...%25Publication&tailor=1&format=iis&noverbal=0  
     
  •  
    http://www.kbs.uni-hannover.de/~steimann/published/EMBM-13-5.pdf 
     
  • Nick Street
    http://dollar.biz.uiowa.edu/~street/research/ 
     

  • http://www.liebertonline.com/doi/pdf/10.1089/10915360152745821      
     
  • Artificial Neural Networks in Individual Cancer Management 
    http://www.nesc.ac.uk/action/projects/project_action.cfm?Title=66  

    The objective is to develop and train a system of artificial neuronal networks (ANNs) providing information on prognosis, staging and optimal (multidisciplinary) management in patients with (a) breast, (b) upper GI and (c) colorectal cancers. In the first part of the study, the NNs system will be developed based on the existing system used by the particle physicists in Manchester by a dedicated postdoc RA working in cooperation with academic clinical oncologists and a research nurse in Dundee using three national patient data bases that are available for the project. The patient data used for development and training of the ANNs will be obtained by random retrieval from the three data bases. In the second part of the project, validation of the ANNs for Cancer Management will be evaluated (in terms of staging, predicted optimal treatment and prognosis) with the observed management and outcome of the patients. 
  •  
     http://www.comp.nus.edu.sg/~rudys/publications.html 
     
  •  
    http://www.comp.nus.edu.sg/%7Epris/  
     
  • IEEE/EURASIP International Workshop on Genomic Signal Processing and Statistics' 2005
    http://www2.mdanderson.org/app/ilya/books.htm 
     
  • Clinical Validation of an automated system for supporting the early diagnosis of melanoma 
    A. Sboner, P. Bauer, G. Zumiani, C. Eccher, E. Blanzieri, S. Forti, and M. Cristofolini 
    Skin Res Technol 10(3):184-192, 2004. 
     
  • A Combined Human-Computer Method for Building Effective Decision Support System 
    A. Sboner, I. Azzini, F. Demichelis, P. Bauer, R. Bellazzi, P. Carli, M. Cristofolini 
    Working notes of 9th Workshop of Intelligent Data Analysis in Medicine and Pharmacology,
    IDAMAP, Stanford, CA, September 6, 2004 
     
  • A Web-based System for Tissue Microarray: Fostering Biomedical Research 
    F. Demichelis, A. Sboner, R. Dell'Anna, J. Santi, ITC-irst, M. Barbareschi, and A. Graiff 
    Medinfo 2004; 2004(CD):1571, IOS Press, 2004.

11-12-2004

New 23-10-2004

New 13-10-2003

New 05-10-2004

New 04-10-2004

New 01-10-2004

New 20-09-2004

New 17-09-2004

New 3-08-2004

New 02-07-2004

28-06-2004

New 25-06-2004

Weekend

Misc.

Fractals

Grid

Misc

Unsorted

DNA Computing

Medical

Software development

More

 

Grid

Misc

Unsorted

Medical

More

New

New

Condor

Powell's multidimensional optimization

Super Clusters

Tools

Tools

GT4

 

Logistic Regression

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E-Grid

  • The Grid: Blueprint for a Future Computing Infrastructure
    Foster I. and Kesselman C. (editors)
    Morgan Kaufmann Publishers, USA, 1999.
     
  • Infrastructure for Science Portals
    Smarr L.
    IEEE Internet Computing, January/February 2000, 71-73.
     
  • Information Power Grid: The new frontier in parallel computing?
    Leinberger W., Kumar V.
    IEEE Concurrency, October-December 1999, 75-84
     
  • Special Issue on Metacomputing: From Workstation Clusters to Internet computing
    Gentzsch W. (editor)
    Future Generation Computer Systems, No. 15, North Holland, 1999. 
     
  • The Grid: International Efforts in Global Computing
    Baker M., Buyya R., and Laforenza D.
    Proceedings of SSGRR 2000 Computer and eBusiness Conference, Scuola Superiore G. Reiss Romoli, L'Aquila, July 31 - August 6, 2000

Websites

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Video Steaming...

Medical

 

2006 - SPMC / SoCCE / UoP