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Alex J. Smola: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Arthur Gretton, Olivier Bousquet, Alex J. Smola, Bernhard Schölkopf
    Measuring Statistical Dependence with Hilbert-Schmidt Norms. [Citation Graph (0, 0)][DBLP]
    ALT, 2005, pp:63-77 [Conf]
  2. Jyrki Kivinen, Alex J. Smola, Robert C. Williamson
    Large Margin Classification for Moving Targets. [Citation Graph (0, 0)][DBLP]
    ALT, 2002, pp:113-127 [Conf]
  3. Bernhard Schölkopf, Ralf Herbrich, Alex J. Smola
    A Generalized Representer Theorem. [Citation Graph (0, 0)][DBLP]
    COLT/EuroCOLT, 2001, pp:416-426 [Conf]
  4. Alex J. Smola, Risi Imre Kondor
    Kernels and Regularization on Graphs. [Citation Graph (0, 0)][DBLP]
    COLT, 2003, pp:144-158 [Conf]
  5. Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf
    Entropy Numbers of Linear Function Classes. [Citation Graph (0, 0)][DBLP]
    COLT, 2000, pp:309-319 [Conf]
  6. Bernhard Schölkopf, Alex J. Smola, Phil Knirsch, Chris Burges
    Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces. [Citation Graph (0, 0)][DBLP]
    DAGM-Symposium, 1998, pp:125-132 [Conf]
  7. Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf
    Entropy Numbers, Operators and Support Vector Kernels. [Citation Graph (0, 0)][DBLP]
    EuroCOLT, 1999, pp:285-299 [Conf]
  8. Alex J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf
    Regularized Principal Manifolds. [Citation Graph (0, 0)][DBLP]
    EuroCOLT, 1999, pp:214-229 [Conf]
  9. Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Christian Lemmen, Alex J. Smola, Thomas Lengauer, Klaus-Robert Müller
    Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites. [Citation Graph (0, 0)][DBLP]
    German Conference on Bioinformatics, 1999, pp:37-43 [Conf]
  10. Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik
    Predicting Time Series with Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:999-1004 [Conf]
  11. Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller
    Kernel Principal Component Analysis. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:583-588 [Conf]
  12. Yasemin Altun, Thomas Hofmann, Alex J. Smola
    Gaussian process classification for segmenting and annotating sequences. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  13. Colin Campbell, Nello Cristianini, Alex J. Smola
    Query Learning with Large Margin Classifiers. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:111-118 [Conf]
  14. Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alex J. Smola
    Multi-Instance Kernels. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:179-186 [Conf]
  15. Quoc V. Le, Alex J. Smola, Thomas Gärtner
    Simpler knowledge-based support vector machines. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:521-528 [Conf]
  16. Julian John McAuley, Tibério S. Caetano, Alex J. Smola, Matthias O. Franz
    Learning high-order MRF priors of color images. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:617-624 [Conf]
  17. Cheng Soon Ong, Alex J. Smola
    Machine Learning with Hyperkernels. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:568-575 [Conf]
  18. Alex J. Smola, Bernhard Schölkopf
    Sparse Greedy Matrix Approximation for Machine Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:911-918 [Conf]
  19. S. V. N. Vishwanathan, Alex J. Smola, M. Narasimha Murty
    SimpleSVM. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:760-767 [Conf]
  20. Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola
    Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments. [Citation Graph (0, 0)][DBLP]
    IJCNN (5), 2000, pp:199-204 [Conf]
  21. Vladimir Nikulin, Alex J. Smola
    Universal Clustering with Regularization in Probabilistic Space. [Citation Graph (0, 0)][DBLP]
    MLDM, 2005, pp:142-152 [Conf]
  22. Bernhard Schölkopf, Alex J. Smola
    A Short Introduction to Learning with Kernels. [Citation Graph (0, 0)][DBLP]
    Machine Learning Summer School, 2002, pp:41-64 [Conf]
  23. Alex J. Smola, Bernhard Schölkopf
    Bayesian Kernel Methods. [Citation Graph (0, 0)][DBLP]
    Machine Learning Summer School, 2002, pp:65-117 [Conf]
  24. Chiranjib Bhattacharyya, Pannagadatta K. Shivaswamy, Alex J. Smola
    A Second Order Cone programming Formulation for Classifying Missing Data. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  25. Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik
    Support Vector Regression Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:155-161 [Conf]
  26. Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, S. V. N. Vishwanathan
    Large-Scale Multiclass Transduction. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  27. Jyrki Kivinen, Alex J. Smola, Robert C. Williamson
    Online Learning with Kernels. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:785-792 [Conf]
  28. Adam Kowalczyk, Alex J. Smola, Robert C. Williamson
    Kernel Machines and Boolean Functions. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:439-446 [Conf]
  29. Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller
    Invariant Feature Extraction and Classification in Kernel Spaces. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:526-532 [Conf]
  30. Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch
    Kernel PCA and De-Noising in Feature Spaces. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:536-542 [Conf]
  31. Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika
    v-Arc: Ensemble Learning in the Presence of Outliers. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:561-567 [Conf]
  32. Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson
    Shrinking the Tube: A New Support Vector Regression Algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:330-336 [Conf]
  33. Bernhard Schölkopf, Patrice Simard, Alex J. Smola, Vladimir Vapnik
    Prior Knowledge in Support Vector Kernels. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  34. Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt
    Support Vector Method for Novelty Detection. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:582-588 [Conf]
  35. Alex J. Smola, Peter L. Bartlett
    Sparse Greedy Gaussian Process Regression. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:619-625 [Conf]
  36. Alex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf
    Semiparametric Support Vector and Linear Programming Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:585-591 [Conf]
  37. Alex J. Smola, Zoltán L. Óvári, Robert C. Williamson
    Regularization with Dot-Product Kernels. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:308-314 [Conf]
  38. Alex J. Smola, Bernhard Schölkopf
    From Regularization Operators to Support Vector Kernels. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  39. Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson
    The Entropy Regularization Information Criterion. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:342-348 [Conf]
  40. Vladimir Vapnik, Steven E. Golowich, Alex J. Smola
    Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:281-287 [Conf]
  41. S. V. N. Vishwanathan, Alex J. Smola
    Binet-Cauchy Kernels. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  42. Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller
    Robust Ensemble Learning for Data Mining. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:341-344 [Conf]
  43. Alex J. Smola, Bernhard Schölkopf
    On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion. [Citation Graph (0, 0)][DBLP]
    Algorithmica, 1998, v:22, n:1/2, pp:211-231 [Journal]
  44. Bernhard Schölkopf, Klaus-Robert Müller, Alex J. Smola
    Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. [Citation Graph (0, 0)][DBLP]
    Inform., Forsch. Entwickl., 1999, v:14, n:3, pp:154-163 [Journal]
  45. Glenn Fung, Olvi L. Mangasarian, Alex J. Smola
    Minimal Kernel Classifiers. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2002, v:3, n:, pp:303-321 [Journal]
  46. Alex J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson
    Regularized Principal Manifolds. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2001, v:1, n:, pp:179-209 [Journal]
  47. S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola
    Step Size Adaptation in Reproducing Kernel Hilbert Space. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:1107-1133 [Journal]
  48. Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, Robert C. Williamson
    Estimating the Support of a High-Dimensional Distribution. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2001, v:13, n:7, pp:1443-1471 [Journal]
  49. Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller
    Nonlinear Component Analysis as a Kernel Eigenvalue Problem. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1998, v:10, n:5, pp:1299-1319 [Journal]
  50. Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett
    New Support Vector Algorithms. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:5, pp:1207-1245 [Journal]
  51. Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola
    Experimentally optimal v in support vector regression for different noise models and parameter settings. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2004, v:17, n:1, pp:127-141 [Journal]
  52. Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola
    Experimentally optimal nu in support vector regression for different noise models and parameter settings. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2005, v:18, n:2, pp:205-0 [Journal]
  53. Alex J. Smola, Bernhard Schölkopf, Klaus-Robert Müller
    The connection between regularization operators and support vector kernels. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1998, v:11, n:4, pp:637-649 [Journal]
  54. Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller
    Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Pattern Anal. Mach. Intell., 2003, v:25, n:5, pp:623-633 [Journal]
  55. Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf
    Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 2001, v:47, n:6, pp:2516-2532 [Journal]
  56. Alex J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf
    A Hilbert Space Embedding for Distributions. [Citation Graph (0, 0)][DBLP]
    ALT, 2007, pp:13-31 [Conf]
  57. Le Song, Alex J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo
    Supervised feature selection via dependence estimation. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:823-830 [Conf]
  58. Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le
    A scalable modular convex solver for regularized risk minimization. [Citation Graph (0, 0)][DBLP]
    KDD, 2007, pp:727-736 [Conf]

  59. Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning. [Citation Graph (, )][DBLP]


  60. Learning Graph Matching. [Citation Graph (, )][DBLP]


  61. Tailoring density estimation via reproducing kernel moment matching. [Citation Graph (, )][DBLP]


  62. Estimating labels from label proportions. [Citation Graph (, )][DBLP]


  63. Colored Maximum Variance Unfolding. [Citation Graph (, )][DBLP]


  64. COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . [Citation Graph (, )][DBLP]


  65. Bundle Methods for Machine Learning. [Citation Graph (, )][DBLP]


  66. A Kernel Statistical Test of Independence. [Citation Graph (, )][DBLP]


  67. Convex Learning with Invariances. [Citation Graph (, )][DBLP]


  68. Kernelized Sorting. [Citation Graph (, )][DBLP]


  69. Kernel Measures of Independence for non-iid Data. [Citation Graph (, )][DBLP]


  70. Improving Maximum Margin Matrix Factorization. [Citation Graph (, )][DBLP]


  71. Learning Graph Matching. [Citation Graph (, )][DBLP]


  72. Adaptive collaborative filtering. [Citation Graph (, )][DBLP]


  73. Semi-Markov Models for Sequence Segmentation. [Citation Graph (, )][DBLP]


  74. Learning Graph Matching [Citation Graph (, )][DBLP]


  75. Feature Hashing for Large Scale Multitask Learning [Citation Graph (, )][DBLP]


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