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Journals in DBLP

Machine Learning
2002, volume: 46, number: 1-3

  1. Nello Cristianini, Colin Campbell, Chris Burges
    Editorial: Kernel Methods: Current Research and Future Directions. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:5-9 [Journal]
  2. Christopher K. I. Williams
    On a Connection between Kernel PCA and Metric Multidimensional Scaling. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:11-19 [Journal]
  3. Peter Sollich
    Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:21-52 [Journal]
  4. Sebastian Risau-Gusman, Mirta B. Gordon
    Hierarchical Learning in Polynomial Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:53-70 [Journal]
  5. Junbin Gao, Steve R. Gunn, Chris J. Harris, Martin Brown
    A Probabilistic Framework for SVM Regression and Error Bar Estimation. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:71-89 [Journal]
  6. Tong Zhang
    On the Dual Formulation of Regularized Linear Systems with Convex Risks. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:91-129 [Journal]
  7. Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee
    Choosing Multiple Parameters for Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:131-159 [Journal]
  8. Dennis DeCoste, Bernhard Schölkopf
    Training Invariant Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:161-190 [Journal]
  9. Yi Lin, Yoonkyung Lee, Grace Wahba
    Support Vector Machines for Classification in Nonstandard Situations. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:191-202 [Journal]
  10. Theodore B. Trafalis, Alexander M. Malyscheff
    An Analytic Center Machine. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:203-223 [Journal]
  11. Ayhan Demiriz, Kristin P. Bennett, John Shawe-Taylor
    Linear Programming Boosting via Column Generation. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:225-254 [Journal]
  12. Olvi L. Mangasarian, David R. Musicant
    Large Scale Kernel Regression via Linear Programming. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:255-269 [Journal]
  13. Gary William Flake, Steve Lawrence
    Efficient SVM Regression Training with SMO. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:271-290 [Journal]
  14. Chih-Wei Hsu, Chih-Jen Lin
    A Simple Decomposition Method for Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:291-314 [Journal]
  15. Pavel Laskov
    Feasible Direction Decomposition Algorithms for Training Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:315-349 [Journal]
  16. S. Sathiya Keerthi, Elmer G. Gilbert
    Convergence of a Generalized SMO Algorithm for SVM Classifier Design. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:351-360 [Journal]
  17. Yi Li, Philip M. Long
    The Relaxed Online Maximum Margin Algorithm. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:361-387 [Journal]
  18. Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik
    Gene Selection for Cancer Classification using Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:389-422 [Journal]
  19. Edda Leopold, Jörg Kindermann
    Text Categorization with Support Vector Machines. How to Represent Texts in Input Space? [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:423-444 [Journal]
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