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davidr's online-learning [50 articles]

Recent papers added to davidr's library classified by the tag online-learning. You can also see everyone's online-learning.
  • Fast online SVD revisions for lightweight recommender systems
    (2003)
    by M Brand
  • On the generalization ability of on-line learning algorithms
    Information Theory, IEEE Transactions on, Vol. 50, No. 9. (2004), pp. 2050-2057.
    posted to generalization online-learning by davidr on 2007-10-08 19:58:58 as **
  • Competitive on-line learning with a convex loss function
    (2 Sep 2005)
    by Vladimir Vovk
    posted to online-learning sequence-prediction by davidr on 2007-09-05 23:25:01 as **
  • Online Bounds for Bayesian Algorithms.
    (2004)
    by Sham M Kakade, Andrew Y Ng
    posted to log-loss online-learning tagged by davidr on 2007-06-06 22:24:15 as **
  • An analysis of logistic models: exponential family connections and online performance
    (2007)
    by Arindam Banerjee
    posted to log-loss online-learning tagged by davidr on 2007-06-06 21:38:44 as **
  • The weighted majority algorithm
    Foundations of Computer Science, 1989., 30th Annual Symposium on (1989), pp. 256-261.
    posted to online-learning by davidr on 2007-06-05 19:24:06 as ** along with 1 person bsilverthorn
  • Competitive On-Line Statistics
    International Statistical Review / Revue Internationale de Statistique, Vol. 69, No. 2. (2001), pp. 213-248.
    by Volodya Vovk
    posted to online-learning tagged by davidr on 2007-06-05 18:27:02 as **
  • On-line regression competitive with reproducing kernel Hilbert spaces
    (24 Jan 2006)
    by Vladimir Vovk
    posted to online-learning regression tagged by davidr on 2007-06-05 17:44:32 as **
  • Mixture models achieving optimal coding regret
    Information Theory Workshop, 1998 (1998), 16.
    by AR Barron, J Takeuchi
    posted to bayesian bma online-learning by davidr on 2007-06-04 23:39:48 as **
  • Information-theoretic characterization of Bayes performance and the choice of priors in parametric and nonparametric problems
    (1998), pp. 27-52.
    by Andrew R Barron
    edited by JM Bernardo, JO Berger, AP Dawid, AFM Smith
    posted to bayesian online-learning by davidr on 2007-06-04 22:24:59 as **
  • Prequential analysis, stochastic complexity and Bayesian inference
    (1992)
    by AP Dawid
    posted to online-learning tagged by davidr on 2007-06-04 19:06:29 as **
  • Information-theoretic asymptotics of Bayes methods
    Information Theory, IEEE Transactions on, Vol. 36, No. 3. (1990), pp. 453-471.
    by BS Clarke, AR Barron
    posted to online-learning tagged by davidr on 2007-06-04 18:47:27 as **
  • How well do Bayes methods work for on-line prediction of $±1$ values?
    No. UCSC-CRL-92-37. (July 1992)
    by David Haussler, A Barron
    posted to online-learning tagged by davidr on 2007-06-04 17:34:02 as **
  • Stochastic Complexity and Modeling
    The Annals of Statistics, Vol. 14, No. 3. (September 1986), pp. 1080-1100.
    by Jorma Rissanen
    posted to online-learning stochastic-complexity tagged by davidr on 2007-06-04 02:44:12 as **
  • Minimax regret under log loss for general classes of experts
    (1999), pp. 12-18.
    by Nicolò Cesa-Bianchi, Gábor Lugosi
    posted to minimax online-learning tagged by davidr on 2007-06-04 01:06:12 as **
  • From $ε$-entropy to KL-entropy: Analysis of minimum information complexity density estimation
    Annals of Statistics, Vol. 34, No. 5. (2006), pp. 2180-2210.
    by Tong Zhang
    posted to bma density-estimation online-learning tagged by davidr on 2007-06-03 18:41:42 as **
  • Learning Bounds for a Generalized Family of Bayesian Posterior Distributions
    by Tong Zhang
    posted to bma online-learning tagged by davidr on 2007-06-03 18:33:54 as **
  • Gaussian regression and optimal finite dimensional linear models
    (1997)
    posted to online-learning regression tagged by davidr on 2007-06-03 18:24:18 as **
  • General bounds on Bayes errors for regression with Gaussian processes
    (1999), pp. 302-308.
    by Manfred Opper, Francesco Vivarelli
    posted to gaussian-process online-learning by davidr on 2007-06-03 18:18:46 as **
  • Advances in Minimum Description Length : Theory and Applications (Neural Information Processing)
    (01 April 2005)
    posted to mdl online-learning by davidr on 2007-06-03 09:13:07 as **
  • Fisher information and stochastic complexity
    Information Theory, IEEE Transactions on, Vol. 42, No. 1. (1996), pp. 40-47.
    by JJ Rissanen
    posted to minimax online-learning stochastic-complexity tagged by davidr on 2007-06-03 01:07:51 as **
  • Optimal sequential probability assignment for individual sequences
    Information Theory, IEEE Transactions on, Vol. 40, No. 2. (1994), pp. 384-396.
    by MJ Weinberger, N Merhav, M Feder
    posted to online-learning tagged by davidr on 2007-06-03 00:53:47 as **
  • Universal noiseless coding
    Information Theory, IEEE Transactions on, Vol. 19, No. 6. (1973), pp. 783-795.
    posted to online-learning tagged by davidr on 2007-06-03 00:47:35 as **
  • Worst-Case Bounds for the Logarithmic Loss of Predictors
    Machine Learning, Vol. 43, No. 3. (1 June 2001), pp. 247-264.
    by Nicolò Cesa-Bianchi, Gábor Lugosi
    posted to online-learning tagged by davidr on 2007-06-03 00:36:03 as **
  • notes The Minimax Strategy for Gaussian Density Estimation
    (2000), pp. 100-106.
    by Eiji Takimoto, Manfred Warmuth
    posted to minimax online-learning tagged by davidr on 2007-06-03 00:07:27 as **
  • Complexity of Nonlogarithmic Loss Functions
    (2001)
    posted to online-learning stochastic-complexity tagged by davidr on 2007-06-03 00:05:30 as **
  • notes Relative Expected Instantaneous Loss Bounds
    Journal of Computer and System Sciences, Vol. 64, No. 1. (February 2002), pp. 76-102.
    by Jurgen Forster, Manfred K Warmuth
    posted to online-learning tagged by davidr on 2007-06-02 23:58:16 as **
  • notes Extended Stochastic Complexity and Minimax Relative Loss Analysis
    Vol. 1720 (1999), pp. 26-38.
    by Kenji Yamanishi
    posted to minimax online-learning stochastic-complexity tagged by davidr on 2007-06-02 23:34:02 as **
  • Estimation and Inference by Compact Coding
    by CS Wallace, PR Freeman
    posted to mml online-learning tagged by davidr on 2007-06-02 23:28:34 as **
  • notes On Relative Loss Bounds in Generalized Linear Regression
    (1999), pp. 269-280.
    by J&\#252;rgen Forster
    posted to online-learning regression tagged by davidr on 2007-06-02 23:16:55 as **
  • Asymptotic minimax regret for data compression, gambling, and prediction
    Information Theory, IEEE Transactions on, Vol. 46, No. 2. (2000), pp. 431-445.
    by Qun Xie, AR Barron
    posted to minimax online-learning tagged by davidr on 2007-06-02 23:14:41 as **
  • A decision-theoretic extension of stochastic complexity and its applications to learning
    Information Theory, IEEE Transactions on, Vol. 44, No. 4. (1998), pp. 1424-1439.
    posted to online-learning stochastic-complexity tagged by davidr on 2007-06-02 23:06:27 as **
  • Estimating the Dimension of a Model
    The Annals of Statistics, Vol. 6, No. 2. (1978), pp. 461-464.
    by Gideon Schwarz
  • Stochastic Complexity
    J. R. Statist. Soc. B, Vol. 49, No. 3. (1987), pp. 223-239.
    by Jorma Rissanen
    posted to nml online-learning tagged by davidr on 2007-06-02 22:34:27 as **
  • Competitive on-line learning with a convex loss function
    (2 Sep 2005)
    by Vladimir Vovk
    posted to online-learning tagged by davidr on 2007-06-02 01:02:59 as ** along with 1 person gagliol
  • Predicting a Binary Sequence Almost As Well As the Optimal Biased Coin
    (1996)
    by Yoav Freund
    posted to online-learning tagged by davidr on 2007-06-01 23:26:03 as **
  • Universal prediction
    Information Theory, IEEE Transactions on, Vol. 44, No. 6. (1998), pp. 2124-2147.
    by N Merhav, M Feder
  • Prediction in the Worst Case
    The Annals of Statistics, Vol. 19, No. 2. (1991), pp. 1084-1090.
    by Dean P Foster
    posted to minimax online-learning tagged by davidr on 2007-06-01 06:01:25 as **
  • Sequential prediction of individual sequences under general loss functions
    IEEE Trans. on Information Theory, Vol. 44, No. 5. (1998), pp. 1906-1925.
    posted to online-learning tagged by davidr on 2007-06-01 05:53:54 as **
  • Mutual information, metric entropy and cumulative relative entropy risk
    (1997)
    by D Haussler, M Opper
    posted to log-loss online-learning tagged by davidr on 2007-06-01 05:50:40 as **
  • Worst case prediction over sequences under log loss
    (1997)
    by M Opper, D Haussler
    posted to log-loss online-learning tagged by davidr on 2007-06-01 05:46:39 as **
  • Aggregating strategies
    (1990), pp. 371-386.
    by Volodimir G Vovk
    posted to online-learning tagged by davidr on 2007-06-01 02:21:30 as **
  • Learning probabilistic prediction functions
    Foundations of Computer Science, 1988., 29th Annual Symposium on (1988), pp. 110-119.
    posted to online-learning tagged by davidr on 2007-06-01 02:17:21 as **
  • Learning probabilistic prediction functions
    (1988), pp. 312-328.
    by Alfredo Desantis, George Markowsky, Mark N Wegman
    posted to online-learning tagged by davidr on 2007-06-01 02:15:41 as **
  • Minimax relative loss analysis for sequential prediction algorithms using parametric hypotheses
    (1998), pp. 32-43.
    by Kenji Yamanishi
    posted to online-learning tagged by davidr on 2007-06-01 00:56:08 as **
  • Competitive on-line linear regression
    (1998), pp. 364-370.
    by V Vovk
    posted to online-learning tagged by davidr on 2007-06-01 00:53:50 as **
  • Competitive on-line statistics
    (1999)
    by V Vovk
    posted to online-learning by davidr on 2007-03-10 23:11:35 as **
  • Online Prediction with Experts under a Log-scoring Rule - Online Expert Prediction
    by B Clarke, AP Dawid
    posted to online-learning sequence-prediction by davidr on 2007-03-10 23:04:28 as **
  • Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions
    Mach. Learn., Vol. 43, No. 3. (June 2001), pp. 211-246.
    by Katy S Azoury, MK Warmuth
    posted to online-learning by davidr on 2006-12-01 18:22:44 as ** along with 1 person yaroslavvb
  • Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions
    Machine Learning, Vol. 43, No. 3. (June 2001), pp. 211-246.
    by KS Azoury, MK Warmuth
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