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hcs machine_learning [16 articles]

最近 hcs さんのライブラリに追加された論文の中から タグ machine_learning. You can also see everyone's machine_learning.
  • Shrink: A Tool for Failure Diagnosis in IP Networks
    by Srikanth Kandula, Dina Katabi, Jean P Vasseur
    posted to machine_learning nws by hcs on 2008-02-01 21:54:04 as **
  • Structure from Failure
    by Ralf
    posted to machine_learning nws by hcs on 2008-02-01 21:27:30 as **
  • Gray’s Anatomy: Dissecting Scanning Activities Using IP Gray Space Analysis
    posted to machine_learning nws by hcs on 2008-02-01 21:20:26 as **
  • Convex optimization techniques for fitting sparse Gaussian graphical models
    (2006), pp. 89-96.
    by Onureena Banerjee, Laurent E Ghaoui, Alexandre D'aspremont, Georges Natsoulis
    posted to graphical_models machine_learning math optimization structure_learning by hcs on 2007-11-15 05:57:15 as **
  • Semi-Supervised Learning -- A Statistical Physics Approach
    (5 Apr 2006)
    by Gad Getz, Noam Shental, Eytan Domany
    posted to free_energy graphical_models machine_learning semi_supervised_learning spectral_graph_theory by hcs on 2007-04-08 18:50:26 as ** along with 1 person proportional
  • Graph based semi supervised learning and spectral design
    by Tong Zhang, Rie Ando
    posted to kernels learning_theory machine_learning semi_supervised_learning spectral_graph_theory by hcs on 2007-04-08 18:48:30 as **
  • Learning Bounds for Kernel Regression Using Effective Data Dimensionality
    Neural Comput., Vol. 17, No. 9. (September 2005), pp. 2077-2098.
    by Tong Zhang
    posted to kernels learning_theory machine_learning semi_supervised_learning spectral_graph_theory by hcs on 2007-04-08 18:31:35 as ** along with 1 person davidr
  • A kernel view of the dimensionality reduction of manifolds
    (2004)
    by J Ham, D Lee, S Mika, B Scholkopf
    posted to learning_theory machine_learning semi_supervised_learning by hcs on 2007-04-08 18:28:10 as ** along with 3 people scis0000001 Scis0000002 sugarexpletive
  • On the relation between low density separation, spectral clustering and graph cuts
    (2006)
    by Hariharan Narayanan, Mikhail Belkin, Partha Niyogi
    posted to learning_theory machine_learning semi_supervised_learning spectral_graph_theory structure_learning by hcs on 2007-03-31 21:37:52 as **
  • Semi-supervised learning literature survey
    by Jerry X Zhu
    posted to machine_learning semi_supervised_learning survey by hcs on 2007-03-31 19:23:22 as **
  • On the Shannon capacity of a graph
    Information Theory, IEEE Transactions on, Vol. 25, No. 1. (1979), pp. 1-7.
    by L Lovasz
    posted to graph_theory information_theory learning_theory machine_learning by hcs on 2007-03-28 20:02:48 as **
  • Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets
    Journal of Artificial Intelligence Research, Vol. 8 (1998), pp. 67-91.
    by Andrew W Moore, Mary S Lee
    posted to graphical_models inference machine_learning symbolic_encoding by hcs on 2007-03-27 20:54:58 as **
  • On the Mathematical Foundations of Learning
    Bulletin of the American Mathematical Society, Vol. 39 (2002), pp. 1-49.
    by F Cucker, S Smale
    posted to learning_theory machine_learning by hcs on 2007-03-27 20:25:03 as **
  • Manifold Regularization : A Geometric Framework for Learning From Examples
    (2004)
    by M Belkin, P Niyogi, V Sindhwani
    posted to learning_theory machine_learning semi_supervised_learning by hcs on 2007-03-27 20:16:50 as ** along with 1 person scis0000001
  • Estimation and Marginalization Using the Kikuchi Approximation Methods
    Neural Computation, Vol. 17, No. 8. (August 2005), pp. 1836-1873.
    by Payam Pakzad, Venkat Anantharam
    posted to inference machine_learning free_energy graphical_models by hcs on 2007-03-10 21:04:41 as **
  • Efficient sparse coding algorithms
    (2006)
    by Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y Ng
    posted to machine_learning by hcs on 2007-03-07 18:10:06 as **
    • 注: このページを引用する時は次のURLでどうぞ: http://www.citeulike.org/user/hcs/tag/machine_learning

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