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sdvillal ml-foundations [22 articles]

最近 sdvillal さんのライブラリに追加された論文の中から タグ ml-foundations. You can also see everyone's ml-foundations.
  • Scale-sensitive dimensions, uniform convergence, and learnability
    J. ACM, Vol. 44, No. 4. (July 1997), pp. 615-631.
    by Noga Alon, Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler
  • Reliable Reasoning: Induction and Statistical Learning Theory (Jean Nicod Lectures)
    (01 May 2007)
    by Gilbert Harman, Sanjeev Kulkarni
  • Tutorial on Practical Prediction Theory for Classification
    Journal of Machine Learning Research, Vol. 6 (March 2005), pp. 273-306.
    by John Langford
    posted to ml-foundations learning-bounds error-estimation by sdvillal on 2008-04-24 12:37:12 as **
  • Abduction and Induction: Essays on their Relation and Integration (Applied Logic Series)
    (30 April 2000)
  • Smart Inductive Generalizations are Abductions
  • Integrating abduction and induction in machine learning
    (1997)
    by R Mooney
  • Convexity, Classification, and Risk Bounds
    Journal of the American Statistical Association, Vol. 101, No. 473. (March 2006), pp. 138-156.
    by Peter L Bartlett, Michael I Jordan, Jon D Mcauliffe
  • On divergences, surrogate loss functions, and decentralized detection
    (25 Oct 2005)
    by Xuanlong Nguyen, Martin J Wainwright, Michael I Jordan
  • Evidence Contrary to the Statistical View of Boosting
    Journal of Machine Learning Research, Vol. 9 (February 2007), pp. 131-156.
    by David Mease, Abraham Wyner
  • Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
    Mach. Learn., Vol. 11, No. 1. (April 1993), pp. 63-90.
    by Robert C Holte
    posted to ml-foundations ml-philosophy simplicity by sdvillal on 2007-06-18 23:20:22 as **
  • The existence of a priori distinctions between learning algorithms
    Neural Comput., Vol. 8, No. 7. (October 1996), pp. 1391-1420.
    by David H Wolpert
    posted to error-estimation free-lunch ml-foundations by sdvillal on 2007-06-17 17:35:05 as **
  • The lack of a priori distinctions between learning algorithms
    Neural Comput., Vol. 8, No. 7. (October 1996), pp. 1341-1390.
    by David H Wolpert
    posted to error-estimation free-lunch ml-foundations by sdvillal on 2007-06-17 17:33:52 as **
  • General conditions for predictivity in learning theory
    Nature, Vol. 428, No. 6981. (25 March 2004), pp. 419-422.
  • Discussion of the paper arcing classifiers by leo breiman
    (1998)
    posted to boosting ensembles error-estimation ml-foundations ml-philosophy by sdvillal on 2007-03-31 22:22:59 as **
  • Stacked Generalization
    No. LA-UR-90-3460. (1990)
    by DH Wolpert
  • Simple classifiers
    (2003)
    by A Cannon, J Howse, D Hush, C Scovel
    posted to ml-foundations by sdvillal on 2007-03-08 19:45:29 as **
  • Multiple instance learning using simple classifiers
    International Conference on Machine Learning and Applications, 2004. Proceedings. 2004 International Conference on (2004), pp. 123-128.
    by A Cannon, D Hush
    posted to ml-foundations by sdvillal on 2007-03-08 19:38:07 as **
  • Statistical Learning Theory
    (16 September 1998)
    by Vladimir N Vapnik
  • The Nature of Statistical Learning Theory (Information Science and Statistics)
    (19 November 1999)
    by Vladimir N Vapnik
  • An overview of statistical learning theory
    Neural Networks, IEEE Transactions on, Vol. 10, No. 5. (1999), pp. 988-999.
    by VN Vapnik
  • E4 - Machine Learning
    by Pedro Domingos
    posted to ml-foundations ml-philosophy by sdvillal on 2007-02-27 10:55:18 as **
  • Towards parameter-free data mining
    (2004), pp. 206-215.
    by Eamonn Keogh, Stefano Lonardi, Chotirat A Ratanamahatana
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