Alexander J. Smola

Alex J. Smola

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2009

113 EE Kilian Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alex J. Smola: Feature Hashing for Large Scale Multitask Learning CoRR abs/0902.2206: (2009)

2008

112 EE Qinfeng Shi, Li Wang, Li Cheng, Alexander J. Smola: Discriminative human action segmentation and recognition using semi-Markov model. CVPR 2008

111 EE Markus Weimer, Alexandros Karatzoglou, Alex J. Smola: Improving Maximum Margin Matrix Factorization. ECML/PKDD (1) 2008: 14

110 EE Ahmed El Zein, Eric McCreath, Alistair P. Rendell, Alex J. Smola: Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning. ICCS (1) 2008: 466-475

109 EE Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le: Estimating labels from label proportions. ICML 2008: 776-783

108 EE Le Song, Xinhua Zhang, Alex J. Smola, Arthur Gretton, Bernhard Schölkopf: Tailoring density estimation via reproducing kernel moment matching. ICML 2008: 992-999

107 EE Julian John McAuley, Tibério S. Caetano, Alexander J. Smola: Robust Near-Isometric Matching via Structured Learning of Graphical Models. NIPS 2008: 1057-1064

106 EE Novi Quadrianto, Le Song, Alex J. Smola: Kernelized Sorting. NIPS 2008: 1289-1296

105 EE Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola: Kernel Measures of Independence for non-iid Data. NIPS 2008: 1937-1944

104 EE Olivier Chapelle, Chuong B. Do, Quoc V. Le, Alexander J. Smola, Choon Hui Teo: Tighter Bounds for Structured Estimation. NIPS 2008: 281-288

103 EE Markus Weimer, Alexandros Karatzoglou, Alex J. Smola: Adaptive collaborative filtering. RecSys 2008: 275-282

102 EE Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola: A Kernel Method for the Two-Sample Problem CoRR abs/0805.2368: (2008)

101 EE Tibério S. Caetano, Julian John McAuley, Li Cheng, Quoc V. Le, Alex J. Smola: Learning Graph Matching CoRR abs/0806.2890: (2008)

100 EE Julian John McAuley, Tibério S. Caetano, Alexander J. Smola: Robust Near-Isometric Matching via Structured Learning of Graphical Models CoRR abs/0809.3618: (2008)

99 EE Markus Weimer, Alexandros Karatzoglou, Alex J. Smola: Improving maximum margin matrix factorization. Machine Learning 72(3): 263-276 (2008)

2007

98 Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola: A Kernel Approach to Comparing Distributions. AAAI 2007: 1637-1641

97 EE Alex J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf: A Hilbert Space Embedding for Distributions. ALT 2007: 13-31

96 EE Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf: A Hilbert Space Embedding for Distributions. Discovery Science 2007: 40-41

95 EE Tibério S. Caetano, Li Cheng, Quoc V. Le, Alex J. Smola: Learning Graph Matching. ICCV 2007: 1-8

94 EE Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt: A dependence maximization view of clustering. ICML 2007: 815-822

93 EE Le Song, Alex J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo: Supervised feature selection via dependence estimation. ICML 2007: 823-830

92 EE Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alexander J. Smola: Gene selection via the BAHSIC family of algorithms. ISMB/ECCB (Supplement of Bioinformatics) 2007: 490-498

91 EE Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le: A scalable modular convex solver for regularized risk minimization. KDD 2007: 727-736

90 EE Alex J. Smola: Learning Graph Matching. MLG 2007

89 EE Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alex J. Smola: A Kernel Statistical Test of Independence. NIPS 2007

88 EE Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le: Bundle Methods for Machine Learning. NIPS 2007

87 EE Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alex J. Smola: COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . NIPS 2007

86 EE Le Song, Alex J. Smola, Karsten M. Borgwardt, Arthur Gretton: Colored Maximum Variance Unfolding. NIPS 2007

85 EE Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola: Convex Learning with Invariances. NIPS 2007

84 EE Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo: Supervised Feature Selection via Dependence Estimation CoRR abs/0704.2668: (2007)

83 EE Quoc V. Le, Alexander J. Smola: Direct Optimization of Ranking Measures CoRR abs/0704.3359: (2007)

82 EE S. V. N. Vishwanathan, Alexander J. Smola, René Vidal: Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes. International Journal of Computer Vision 73(1): 95-119 (2007)

2006

81 EE Yasemin Altun, Alexander J. Smola: Unifying Divergence Minimization and Statistical Inference Via Convex Duality. COLT 2006: 139-153

80 EE Quoc V. Le, Alexander J. Smola, Thomas Gärtner, Yasemin Altun: Transductive Gaussian Process Regression with Automatic Model Selection. ECML 2006: 306-317

79 EE Quoc V. Le, Alex J. Smola, Thomas Gärtner: Simpler knowledge-based support vector machines. ICML 2006: 521-528

78 EE Julian John McAuley, Tibério S. Caetano, Alex J. Smola, Matthias O. Franz: Learning high-order MRF priors of color images. ICML 2006: 617-624

77 EE Hao Shen, Knut Hüper, Alexander J. Smola: Newton-Like Methods for Nonparametric Independent Component Analysis. ICONIP (1) 2006: 1068-1077

76 EE Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola: Integrating structured biological data by Kernel Maximum Mean Discrepancy. ISMB (Supplement of Bioinformatics) 2006: 49-57

75 EE Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola: A Kernel Method for the Two-Sample-Problem. NIPS 2006: 513-520

74 EE Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf: Correcting Sample Selection Bias by Unlabeled Data. NIPS 2006: 601-608

73 EE S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola: Step Size Adaptation in Reproducing Kernel Hilbert Space. Journal of Machine Learning Research 7: 1107-1133 (2006)

72 EE Ichiro Takeuchi, Quoc V. Le, Tim D. Sears, Alexander J. Smola: Nonparametric Quantile Estimation. Journal of Machine Learning Research 7: 1231-1264 (2006)

71 EE Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola: Second Order Cone Programming Approaches for Handling Missing and Uncertain Data. Journal of Machine Learning Research 7: 1283-1314 (2006)

70 EE Stéphane Canu, Alexander J. Smola: Kernel methods and the exponential family. Neurocomputing 69(7-9): 714-720 (2006)

69 EE S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, Alexander J. Smola: Kernel extrapolation. Neurocomputing 69(7-9): 721-729 (2006)

2005

68 EE Arthur Gretton, Olivier Bousquet, Alex J. Smola, Bernhard Schölkopf: Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT 2005: 63-77

67 EE Stéphane Canu, Alexander J. Smola: Kernel methods and the exponential family. ESANN 2005: 447-454

66 EE Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, Alexander J. Smola: Joint Regularization. ESANN 2005: 455-460

65 EE Quoc V. Le, Alexander J. Smola, Stéphane Canu: Heteroscedastic Gaussian process regression. ICML 2005: 489-496

64 EE Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, Hans-Peter Kriegel: Protein function prediction via graph kernels. ISMB (Supplement of Bioinformatics) 2005: 47-56

63 EE Vladimir Nikulin, Alex J. Smola: Universal Clustering with Regularization in Probabilistic Space. MLDM 2005: 142-152

62 EE Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, S. V. N. Vishwanathan: Large-Scale Multiclass Transduction. NIPS 2005

61 EE Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Learning the Kernel with Hyperkernels. Journal of Machine Learning Research 6: 1043-1071 (2005)

60 EE Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf: Kernel Methods for Measuring Independence. Journal of Machine Learning Research 6: 2075-2129 (2005)

59 EE Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola: Experimentally optimal nu in support vector regression for different noise models and parameter settings. Neural Networks 18(2): 205- (2005)

58 EE Gaëlle Loosli, Stéphane Canu, S. V. N. Vishwanathan, Alexander J. Smola, M. Chattopadhyay: Boîte à outils SVM simple et rapide. Revue d'Intelligence Artificielle 19(4-5): 741-767 (2005)

2004

57 EE Yasemin Altun, Thomas Hofmann, Alex J. Smola: Gaussian process classification for segmenting and annotating sequences. ICML 2004

56 EE Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola: Learning with non-positive kernels. ICML 2004

55 EE Chiranjib Bhattacharyya, Pannagadatta K. Shivaswamy, Alex J. Smola: A Second Order Cone programming Formulation for Classifying Missing Data. NIPS 2004

54 EE S. V. N. Vishwanathan, Alex J. Smola: Binet-Cauchy Kernels. NIPS 2004

53 EE Yasemin Altun, Alexander J. Smola, Thomas Hofmann: Exponential Families for Conditional Random Fields. UAI 2004: 2-9

52 EE Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola: Experimentally optimal v in support vector regression for different noise models and parameter settings. Neural Networks 17(1): 127-141 (2004)

51 EE Alexander J. Smola, Bernhard Schölkopf: A tutorial on support vector regression. Statistics and Computing 14(3): 199-222 (2004)

2003

50 Shahar Mendelson, Alex J. Smola: Advanced Lectures on Machine Learning, Machine Learning Summer School 2002, Canberra, Australia, February 11-22, 2002, Revised Lectures Springer 2003

49 EE Alex J. Smola, Risi Imre Kondor: Kernels and Regularization on Graphs. COLT 2003: 144-158

48 Cheng Soon Ong, Alex J. Smola: Machine Learning with Hyperkernels. ICML 2003: 568-575

47 S. V. N. Vishwanathan, Alex J. Smola, M. Narasimha Murty: SimpleSVM. ICML 2003: 760-767

46 EE Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin: Laplace Propagation. NIPS 2003

45 EE 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. IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 623-633 (2003)

2002

44 EE Jyrki Kivinen, Alex J. Smola, Robert C. Williamson: Large Margin Classification for Moving Targets. ALT 2002: 113-127

43 Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alex J. Smola: Multi-Instance Kernels. ICML 2002: 179-186

42 EE Bernhard Schölkopf, Alex J. Smola: A Short Introduction to Learning with Kernels. Machine Learning Summer School 2002: 41-64

41 EE Alex J. Smola, Bernhard Schölkopf: Bayesian Kernel Methods. Machine Learning Summer School 2002: 65-117

40 EE Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Hyperkernels. NIPS 2002: 478-485

39 EE Gunnar Rätsch, Alexander J. Smola, Sebastian Mika: Adapting Codes and Embeddings for Polychotomies. NIPS 2002: 513-520

38 EE S. V. N. Vishwanathan, Alexander J. Smola: Fast Kernels for String and Tree Matching. NIPS 2002: 569-576

37 EE Glenn Fung, Olvi L. Mangasarian, Alex J. Smola: Minimal Kernel Classifiers. Journal of Machine Learning Research 3: 303-321 (2002)

2001

36 EE Bernhard Schölkopf, Ralf Herbrich, Alex J. Smola: A Generalized Representer Theorem. COLT/EuroCOLT 2001: 416-426

35 EE Adam Kowalczyk, Alex J. Smola, Robert C. Williamson: Kernel Machines and Boolean Functions. NIPS 2001: 439-446

34 EE Jyrki Kivinen, Alex J. Smola, Robert C. Williamson: Online Learning with Kernels. NIPS 2001: 785-792

33 Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. IEEE Transactions on Information Theory 47(6): 2516-2532 (2001)

32 EE Alex J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson: Regularized Principal Manifolds. Journal of Machine Learning Research 1: 179-209 (2001)

31 Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, Robert C. Williamson: Estimating the Support of a High-Dimensional Distribution. Neural Computation 13(7): 1443-1471 (2001)

2000

30 Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Entropy Numbers of Linear Function Classes. COLT 2000: 309-319

29 Colin Campbell, Nello Cristianini, Alex J. Smola: Query Learning with Large Margin Classifiers. ICML 2000: 111-118

28 Alex J. Smola, Bernhard Schölkopf: Sparse Greedy Matrix Approximation for Machine Learning. ICML 2000: 911-918

27 EE Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola: Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments. IJCNN (5) 2000: 199-204

26 Alex J. Smola, Zoltán L. Óvári, Robert C. Williamson: Regularization with Dot-Product Kernels. NIPS 2000: 308-314

25 Alex J. Smola, Peter L. Bartlett: Sparse Greedy Gaussian Process Regression. NIPS 2000: 619-625

24 Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller: Robust Ensemble Learning for Data Mining. PAKDD 2000: 341-344

23 Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett: New Support Vector Algorithms. Neural Computation 12(5): 1207-1245 (2000)

1999

22 EE Alex J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf: Regularized Principal Manifolds. EuroCOLT 1999: 214-229

21 EE Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Entropy Numbers, Operators and Support Vector Kernels. EuroCOLT 1999: 285-299

20 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. German Conference on Bioinformatics 1999: 37-43

19 EE Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson: The Entropy Regularization Information Criterion. NIPS 1999: 342-348

18 EE Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller: Invariant Feature Extraction and Classification in Kernel Spaces. NIPS 1999: 526-532

17 EE Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika: v-Arc: Ensemble Learning in the Presence of Outliers. NIPS 1999: 561-567

16 EE Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt: Support Vector Method for Novelty Detection. NIPS 1999: 582-588

15 EE Bernhard Schölkopf, Sebastian Mika, Christopher J. C. Burges, Phil Knirsch, Klaus-Robert Müller, Gunnar Rätsch, Alexander J. Smola: Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks 10(5): 1000-1017 (1999)

14 EE Bernhard Schölkopf, Klaus-Robert Müller, Alex J. Smola: Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. Inform., Forsch. Entwickl. 14(3): 154-163 (1999)

1998

13 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. DAGM-Symposium 1998: 125-132

12 EE Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson: Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336

11 EE Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch: Kernel PCA and De-Noising in Feature Spaces. NIPS 1998: 536-542

10 EE Alex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf: Semiparametric Support Vector and Linear Programming Machines. NIPS 1998: 585-591

9 Alex J. Smola, Bernhard Schölkopf: On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion. Algorithmica 22(1/2): 211-231 (1998)

8 Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10(5): 1299-1319 (1998)

7 EE Alex J. Smola, Bernhard Schölkopf, Klaus-Robert Müller: The connection between regularization operators and support vector kernels. Neural Networks 11(4): 637-649 (1998)

1997

6 Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller: Kernel Principal Component Analysis. ICANN 1997: 583-588

5 Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik: Predicting Time Series with Support Vector Machines. ICANN 1997: 999-1004

4 Alex J. Smola, Bernhard Schölkopf: From Regularization Operators to Support Vector Kernels. NIPS 1997

3 Bernhard Schölkopf, Patrice Simard, Alex J. Smola, Vladimir Vapnik: Prior Knowledge in Support Vector Kernels. NIPS 1997

1996

2 EE Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik: Support Vector Regression Machines. NIPS 1996: 155-161

1 EE Vladimir Vapnik, Steven E. Golowich, Alex J. Smola: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. NIPS 1996: 281-287

2009
113	EE	Kilian Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alex J. Smola: Feature Hashing for Large Scale Multitask Learning CoRR abs/0902.2206: (2009)
2008
112	EE	Qinfeng Shi, Li Wang, Li Cheng, Alexander J. Smola: Discriminative human action segmentation and recognition using semi-Markov model. CVPR 2008
111	EE	Markus Weimer, Alexandros Karatzoglou, Alex J. Smola: Improving Maximum Margin Matrix Factorization. ECML/PKDD (1) 2008: 14
110	EE	Ahmed El Zein, Eric McCreath, Alistair P. Rendell, Alex J. Smola: Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning. ICCS (1) 2008: 466-475
109	EE	Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le: Estimating labels from label proportions. ICML 2008: 776-783
108	EE	Le Song, Xinhua Zhang, Alex J. Smola, Arthur Gretton, Bernhard Schölkopf: Tailoring density estimation via reproducing kernel moment matching. ICML 2008: 992-999
107	EE	Julian John McAuley, Tibério S. Caetano, Alexander J. Smola: Robust Near-Isometric Matching via Structured Learning of Graphical Models. NIPS 2008: 1057-1064
106	EE	Novi Quadrianto, Le Song, Alex J. Smola: Kernelized Sorting. NIPS 2008: 1289-1296
105	EE	Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola: Kernel Measures of Independence for non-iid Data. NIPS 2008: 1937-1944
104	EE	Olivier Chapelle, Chuong B. Do, Quoc V. Le, Alexander J. Smola, Choon Hui Teo: Tighter Bounds for Structured Estimation. NIPS 2008: 281-288
103	EE	Markus Weimer, Alexandros Karatzoglou, Alex J. Smola: Adaptive collaborative filtering. RecSys 2008: 275-282
102	EE	Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola: A Kernel Method for the Two-Sample Problem CoRR abs/0805.2368: (2008)
101	EE	Tibério S. Caetano, Julian John McAuley, Li Cheng, Quoc V. Le, Alex J. Smola: Learning Graph Matching CoRR abs/0806.2890: (2008)
100	EE	Julian John McAuley, Tibério S. Caetano, Alexander J. Smola: Robust Near-Isometric Matching via Structured Learning of Graphical Models CoRR abs/0809.3618: (2008)
99	EE	Markus Weimer, Alexandros Karatzoglou, Alex J. Smola: Improving maximum margin matrix factorization. Machine Learning 72(3): 263-276 (2008)
2007
98		Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola: A Kernel Approach to Comparing Distributions. AAAI 2007: 1637-1641
97	EE	Alex J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf: A Hilbert Space Embedding for Distributions. ALT 2007: 13-31
96	EE	Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf: A Hilbert Space Embedding for Distributions. Discovery Science 2007: 40-41
95	EE	Tibério S. Caetano, Li Cheng, Quoc V. Le, Alex J. Smola: Learning Graph Matching. ICCV 2007: 1-8
94	EE	Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt: A dependence maximization view of clustering. ICML 2007: 815-822
93	EE	Le Song, Alex J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo: Supervised feature selection via dependence estimation. ICML 2007: 823-830
92	EE	Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alexander J. Smola: Gene selection via the BAHSIC family of algorithms. ISMB/ECCB (Supplement of Bioinformatics) 2007: 490-498
91	EE	Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le: A scalable modular convex solver for regularized risk minimization. KDD 2007: 727-736
90	EE	Alex J. Smola: Learning Graph Matching. MLG 2007
89	EE	Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alex J. Smola: A Kernel Statistical Test of Independence. NIPS 2007
88	EE	Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le: Bundle Methods for Machine Learning. NIPS 2007
87	EE	Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alex J. Smola: COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . NIPS 2007
86	EE	Le Song, Alex J. Smola, Karsten M. Borgwardt, Arthur Gretton: Colored Maximum Variance Unfolding. NIPS 2007
85	EE	Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola: Convex Learning with Invariances. NIPS 2007
84	EE	Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo: Supervised Feature Selection via Dependence Estimation CoRR abs/0704.2668: (2007)
83	EE	Quoc V. Le, Alexander J. Smola: Direct Optimization of Ranking Measures CoRR abs/0704.3359: (2007)
82	EE	S. V. N. Vishwanathan, Alexander J. Smola, René Vidal: Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes. International Journal of Computer Vision 73(1): 95-119 (2007)
2006
81	EE	Yasemin Altun, Alexander J. Smola: Unifying Divergence Minimization and Statistical Inference Via Convex Duality. COLT 2006: 139-153
80	EE	Quoc V. Le, Alexander J. Smola, Thomas Gärtner, Yasemin Altun: Transductive Gaussian Process Regression with Automatic Model Selection. ECML 2006: 306-317
79	EE	Quoc V. Le, Alex J. Smola, Thomas Gärtner: Simpler knowledge-based support vector machines. ICML 2006: 521-528
78	EE	Julian John McAuley, Tibério S. Caetano, Alex J. Smola, Matthias O. Franz: Learning high-order MRF priors of color images. ICML 2006: 617-624
77	EE	Hao Shen, Knut Hüper, Alexander J. Smola: Newton-Like Methods for Nonparametric Independent Component Analysis. ICONIP (1) 2006: 1068-1077
76	EE	Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola: Integrating structured biological data by Kernel Maximum Mean Discrepancy. ISMB (Supplement of Bioinformatics) 2006: 49-57
75	EE	Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola: A Kernel Method for the Two-Sample-Problem. NIPS 2006: 513-520
74	EE	Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf: Correcting Sample Selection Bias by Unlabeled Data. NIPS 2006: 601-608
73	EE	S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola: Step Size Adaptation in Reproducing Kernel Hilbert Space. Journal of Machine Learning Research 7: 1107-1133 (2006)
72	EE	Ichiro Takeuchi, Quoc V. Le, Tim D. Sears, Alexander J. Smola: Nonparametric Quantile Estimation. Journal of Machine Learning Research 7: 1231-1264 (2006)
71	EE	Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola: Second Order Cone Programming Approaches for Handling Missing and Uncertain Data. Journal of Machine Learning Research 7: 1283-1314 (2006)
70	EE	Stéphane Canu, Alexander J. Smola: Kernel methods and the exponential family. Neurocomputing 69(7-9): 714-720 (2006)
69	EE	S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, Alexander J. Smola: Kernel extrapolation. Neurocomputing 69(7-9): 721-729 (2006)
2005
68	EE	Arthur Gretton, Olivier Bousquet, Alex J. Smola, Bernhard Schölkopf: Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT 2005: 63-77
67	EE	Stéphane Canu, Alexander J. Smola: Kernel methods and the exponential family. ESANN 2005: 447-454
66	EE	Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, Alexander J. Smola: Joint Regularization. ESANN 2005: 455-460
65	EE	Quoc V. Le, Alexander J. Smola, Stéphane Canu: Heteroscedastic Gaussian process regression. ICML 2005: 489-496
64	EE	Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, Hans-Peter Kriegel: Protein function prediction via graph kernels. ISMB (Supplement of Bioinformatics) 2005: 47-56
63	EE	Vladimir Nikulin, Alex J. Smola: Universal Clustering with Regularization in Probabilistic Space. MLDM 2005: 142-152
62	EE	Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, S. V. N. Vishwanathan: Large-Scale Multiclass Transduction. NIPS 2005
61	EE	Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Learning the Kernel with Hyperkernels. Journal of Machine Learning Research 6: 1043-1071 (2005)
60	EE	Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf: Kernel Methods for Measuring Independence. Journal of Machine Learning Research 6: 2075-2129 (2005)
59	EE	Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola: Experimentally optimal nu in support vector regression for different noise models and parameter settings. Neural Networks 18(2): 205- (2005)
58	EE	Gaëlle Loosli, Stéphane Canu, S. V. N. Vishwanathan, Alexander J. Smola, M. Chattopadhyay: Boîte à outils SVM simple et rapide. Revue d'Intelligence Artificielle 19(4-5): 741-767 (2005)
2004
57	EE	Yasemin Altun, Thomas Hofmann, Alex J. Smola: Gaussian process classification for segmenting and annotating sequences. ICML 2004
56	EE	Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola: Learning with non-positive kernels. ICML 2004
55	EE	Chiranjib Bhattacharyya, Pannagadatta K. Shivaswamy, Alex J. Smola: A Second Order Cone programming Formulation for Classifying Missing Data. NIPS 2004
54	EE	S. V. N. Vishwanathan, Alex J. Smola: Binet-Cauchy Kernels. NIPS 2004
53	EE	Yasemin Altun, Alexander J. Smola, Thomas Hofmann: Exponential Families for Conditional Random Fields. UAI 2004: 2-9
52	EE	Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola: Experimentally optimal v in support vector regression for different noise models and parameter settings. Neural Networks 17(1): 127-141 (2004)
51	EE	Alexander J. Smola, Bernhard Schölkopf: A tutorial on support vector regression. Statistics and Computing 14(3): 199-222 (2004)
2003
50		Shahar Mendelson, Alex J. Smola: Advanced Lectures on Machine Learning, Machine Learning Summer School 2002, Canberra, Australia, February 11-22, 2002, Revised Lectures Springer 2003
49	EE	Alex J. Smola, Risi Imre Kondor: Kernels and Regularization on Graphs. COLT 2003: 144-158
48		Cheng Soon Ong, Alex J. Smola: Machine Learning with Hyperkernels. ICML 2003: 568-575
47		S. V. N. Vishwanathan, Alex J. Smola, M. Narasimha Murty: SimpleSVM. ICML 2003: 760-767
46	EE	Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin: Laplace Propagation. NIPS 2003
45	EE	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. IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 623-633 (2003)
2002
44	EE	Jyrki Kivinen, Alex J. Smola, Robert C. Williamson: Large Margin Classification for Moving Targets. ALT 2002: 113-127
43		Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alex J. Smola: Multi-Instance Kernels. ICML 2002: 179-186
42	EE	Bernhard Schölkopf, Alex J. Smola: A Short Introduction to Learning with Kernels. Machine Learning Summer School 2002: 41-64
41	EE	Alex J. Smola, Bernhard Schölkopf: Bayesian Kernel Methods. Machine Learning Summer School 2002: 65-117
40	EE	Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Hyperkernels. NIPS 2002: 478-485
39	EE	Gunnar Rätsch, Alexander J. Smola, Sebastian Mika: Adapting Codes and Embeddings for Polychotomies. NIPS 2002: 513-520
38	EE	S. V. N. Vishwanathan, Alexander J. Smola: Fast Kernels for String and Tree Matching. NIPS 2002: 569-576
37	EE	Glenn Fung, Olvi L. Mangasarian, Alex J. Smola: Minimal Kernel Classifiers. Journal of Machine Learning Research 3: 303-321 (2002)
2001
36	EE	Bernhard Schölkopf, Ralf Herbrich, Alex J. Smola: A Generalized Representer Theorem. COLT/EuroCOLT 2001: 416-426
35	EE	Adam Kowalczyk, Alex J. Smola, Robert C. Williamson: Kernel Machines and Boolean Functions. NIPS 2001: 439-446
34	EE	Jyrki Kivinen, Alex J. Smola, Robert C. Williamson: Online Learning with Kernels. NIPS 2001: 785-792
33		Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. IEEE Transactions on Information Theory 47(6): 2516-2532 (2001)
32	EE	Alex J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson: Regularized Principal Manifolds. Journal of Machine Learning Research 1: 179-209 (2001)
31		Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, Robert C. Williamson: Estimating the Support of a High-Dimensional Distribution. Neural Computation 13(7): 1443-1471 (2001)
2000
30		Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Entropy Numbers of Linear Function Classes. COLT 2000: 309-319
29		Colin Campbell, Nello Cristianini, Alex J. Smola: Query Learning with Large Margin Classifiers. ICML 2000: 111-118
28		Alex J. Smola, Bernhard Schölkopf: Sparse Greedy Matrix Approximation for Machine Learning. ICML 2000: 911-918
27	EE	Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola: Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments. IJCNN (5) 2000: 199-204
26		Alex J. Smola, Zoltán L. Óvári, Robert C. Williamson: Regularization with Dot-Product Kernels. NIPS 2000: 308-314
25		Alex J. Smola, Peter L. Bartlett: Sparse Greedy Gaussian Process Regression. NIPS 2000: 619-625
24		Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller: Robust Ensemble Learning for Data Mining. PAKDD 2000: 341-344
23		Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett: New Support Vector Algorithms. Neural Computation 12(5): 1207-1245 (2000)
1999
22	EE	Alex J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf: Regularized Principal Manifolds. EuroCOLT 1999: 214-229
21	EE	Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Entropy Numbers, Operators and Support Vector Kernels. EuroCOLT 1999: 285-299
20		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. German Conference on Bioinformatics 1999: 37-43
19	EE	Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson: The Entropy Regularization Information Criterion. NIPS 1999: 342-348
18	EE	Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller: Invariant Feature Extraction and Classification in Kernel Spaces. NIPS 1999: 526-532
17	EE	Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika: v-Arc: Ensemble Learning in the Presence of Outliers. NIPS 1999: 561-567
16	EE	Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt: Support Vector Method for Novelty Detection. NIPS 1999: 582-588
15	EE	Bernhard Schölkopf, Sebastian Mika, Christopher J. C. Burges, Phil Knirsch, Klaus-Robert Müller, Gunnar Rätsch, Alexander J. Smola: Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks 10(5): 1000-1017 (1999)
14	EE	Bernhard Schölkopf, Klaus-Robert Müller, Alex J. Smola: Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. Inform., Forsch. Entwickl. 14(3): 154-163 (1999)
1998
13		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. DAGM-Symposium 1998: 125-132
12	EE	Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson: Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336
11	EE	Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch: Kernel PCA and De-Noising in Feature Spaces. NIPS 1998: 536-542
10	EE	Alex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf: Semiparametric Support Vector and Linear Programming Machines. NIPS 1998: 585-591
9		Alex J. Smola, Bernhard Schölkopf: On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion. Algorithmica 22(1/2): 211-231 (1998)
8		Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10(5): 1299-1319 (1998)
7	EE	Alex J. Smola, Bernhard Schölkopf, Klaus-Robert Müller: The connection between regularization operators and support vector kernels. Neural Networks 11(4): 637-649 (1998)
1997
6		Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller: Kernel Principal Component Analysis. ICANN 1997: 583-588
5		Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik: Predicting Time Series with Support Vector Machines. ICANN 1997: 999-1004
4		Alex J. Smola, Bernhard Schölkopf: From Regularization Operators to Support Vector Kernels. NIPS 1997
3		Bernhard Schölkopf, Patrice Simard, Alex J. Smola, Vladimir Vapnik: Prior Knowledge in Support Vector Kernels. NIPS 1997
1996
2	EE	Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik: Support Vector Regression Machines. NIPS 1996: 155-161
1	EE	Vladimir Vapnik, Steven E. Golowich, Alex J. Smola: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. NIPS 1996: 281-287

Coauthor Index

1 Yasemin Altun [53] [57] [80] [81]

2 Josh Attenberg [113]

3 Peter L. Bartlett [12] [23] [25]

4 Justin Bedo [84] [92] [93]

5 Chiranjib Bhattacharyya (Chiru Bhattacharyya) [55] [71]

6 Karsten M. Borgwardt [64] [66] [69] [74] [75] [76] [84] [86] [92] [93] [94] [98] [102]

7 Olivier Bousquet [60] [68]

8 Christopher J. C. Burges (Chris Burges) [2] [13] [15]

9 Simon Burton [62]

10 Tibério S. Caetano [78] [95] [100] [101] [107] [109]

11 Colin Campbell [29]

12 Stéphane Canu [56] [58] [65] [67] [70]

13 Athanassia Chalimourda [27] [52] [59]

14 Olivier Chapelle [104]

15 M. Chattopadhyay [58]

16 Li Cheng [95] [101] [112]

17 Nello Cristianini [29]

18 Anirban Dasgupta [113]

19 Chuong B. Do [104]

20 Harris Drucker [2]

21 Eleazar Eskin [46]

22 Peter A. Flach [43]

23 Matthias O. Franz [78]

24 Thilo-Thomas Frieß [10]

25 Kenji Fukumizu [89]

26 Glenn Fung [37]

27 Thomas Gärtner [43] [62] [79] [80]

28 Amir Globerson [85]

29 Steven E. Golowich [1]

30 Arthur Gretton [60] [68] [74] [75] [76] [84] [86] [89] [92] [93] [94] [96] [97] [98] [102] [105] [108]

31 Omri Guttman [66] [69]

32 Ralf Herbrich [36] [60]

33 Thomas Hofmann [53] [57]

34 Jiayuan Huang [74]

35 Knut Hüper [77]

36 Alexandros Karatzoglou [87] [99] [103] [111]

37 Linda Kaufman [2]

38 Jyrki Kivinen [34] [44]

39 Phil Knirsch [13] [15]

40 Jens Kohlmorgen [5]

41 Risi Imre Kondor [49]

42 Adam Kowalczyk [35] [43]

43 Hans-Peter Kriegel [64] [76]

44 John Langford [113]

45 Quoc V. Le [62] [65] [72] [79] [80] [83] [87] [88] [91] [95] [101] [104] [109]

46 Christian Lemmen [20]

47 Thomas Lengauer [20]

48 Gaëlle Loosli [58]

49 Olvi L. Mangasarian (O. L. Mangasarian) [37]

50 Xavier Mary [56]

51 Julian John McAuley [78] [100] [101] [107]

52 Eric McCreath [110]

53 Shahar Mendelson [50]

54 Sebastian Mika [11] [15] [17] [18] [20] [22] [24] [32] [39] [45]

55 Klaus-Robert Müller [5] [6] [7] [8] [11] [14] [15] [17] [18] [20] [24] [45]

56 M. Narasimha Murty [47]

57 Vladimir Nikulin [63]

58 Cheng Soon Ong [40] [48] [56] [61] [64]

59 Takashi Onoda [17] [24]

60 Zoltán L. Óvári [26]

61 John C. Platt [16] [31]

62 Novi Quadrianto [106] [109]

63 Malte J. Rasch [75] [76] [98] [102]

64 Gunnar Rätsch [5] [11] [15] [17] [18] [20] [24] [39] [45]

65 Alistair P. Rendell [110]

66 Sam T. Roweis [85]

67 Bernhard Schölkopf [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [27] [28] [30] [31] [32] [33] [36] [41] [42] [45] [51] [52] [59] [60] [68] [74] [75] [76] [89] [96] [97] [98] [102] [108]

68 Matthias Scholz [11]

69 Stefan Schönauer [64]

70 Nicol N. Schraudolph [73]

71 Tim D. Sears [72]

72 John Shawe-Taylor [16] [19] [31]

73 Hao Shen [77]

74 Qinfeng Shi [112]

75 Pannagadatta K. Shivaswamy [55] [71]

76 Patrice Y. Simard (Patrice Simard) [3]

77 Le Song [84] [86] [89] [92] [93] [94] [96] [97] [105] [106] [108]

78 Ichiro Takeuchi [72]

79 Choon Hui Teo [85] [89] [91] [104]

80 Vladimir Vapnik [1] [2] [3] [5]

81 René Vidal [82]

82 S. V. N. Vishwanathan (Vishy Vishwanathan) [38] [46] [47] [54] [58] [62] [64] [66] [69] [73] [82] [88] [91]

83 Li Wang [112]

84 Markus Weimer [87] [99] [103] [111]

85 Kilian Weinberger [113]

86 Jason Weston [18] [45]

87 Robert C. Williamson [12] [16] [19] [21] [22] [23] [26] [30] [31] [32] [33] [34] [35] [40] [44] [61]

88 Ahmed El Zein [110]

89 Xinhua Zhang [105] [108]

90 Alexander Zien [20]

Colors in the list of coauthors

1	Yasemin Altun	[53] [57] [80] [81]
2	Josh Attenberg	[113]
3	Peter L. Bartlett	[12] [23] [25]
4	Justin Bedo	[84] [92] [93]
5	Chiranjib Bhattacharyya (Chiru Bhattacharyya)	[55] [71]
6	Karsten M. Borgwardt	[64] [66] [69] [74] [75] [76] [84] [86] [92] [93] [94] [98] [102]
7	Olivier Bousquet	[60] [68]
8	Christopher J. C. Burges (Chris Burges)	[2] [13] [15]
9	Simon Burton	[62]
10	Tibério S. Caetano	[78] [95] [100] [101] [107] [109]
11	Colin Campbell	[29]
12	Stéphane Canu	[56] [58] [65] [67] [70]
13	Athanassia Chalimourda	[27] [52] [59]
14	Olivier Chapelle	[104]
15	M. Chattopadhyay	[58]
16	Li Cheng	[95] [101] [112]
17	Nello Cristianini	[29]
18	Anirban Dasgupta	[113]
19	Chuong B. Do	[104]
20	Harris Drucker	[2]
21	Eleazar Eskin	[46]
22	Peter A. Flach	[43]
23	Matthias O. Franz	[78]
24	Thilo-Thomas Frieß	[10]
25	Kenji Fukumizu	[89]
26	Glenn Fung	[37]
27	Thomas Gärtner	[43] [62] [79] [80]
28	Amir Globerson	[85]
29	Steven E. Golowich	[1]
30	Arthur Gretton	[60] [68] [74] [75] [76] [84] [86] [89] [92] [93] [94] [96] [97] [98] [102] [105] [108]
31	Omri Guttman	[66] [69]
32	Ralf Herbrich	[36] [60]
33	Thomas Hofmann	[53] [57]
34	Jiayuan Huang	[74]
35	Knut Hüper	[77]
36	Alexandros Karatzoglou	[87] [99] [103] [111]
37	Linda Kaufman	[2]
38	Jyrki Kivinen	[34] [44]
39	Phil Knirsch	[13] [15]
40	Jens Kohlmorgen	[5]
41	Risi Imre Kondor	[49]
42	Adam Kowalczyk	[35] [43]
43	Hans-Peter Kriegel	[64] [76]
44	John Langford	[113]
45	Quoc V. Le	[62] [65] [72] [79] [80] [83] [87] [88] [91] [95] [101] [104] [109]
46	Christian Lemmen	[20]
47	Thomas Lengauer	[20]
48	Gaëlle Loosli	[58]
49	Olvi L. Mangasarian (O. L. Mangasarian)	[37]
50	Xavier Mary	[56]
51	Julian John McAuley	[78] [100] [101] [107]
52	Eric McCreath	[110]
53	Shahar Mendelson	[50]
54	Sebastian Mika	[11] [15] [17] [18] [20] [22] [24] [32] [39] [45]
55	Klaus-Robert Müller	[5] [6] [7] [8] [11] [14] [15] [17] [18] [20] [24] [45]
56	M. Narasimha Murty	[47]
57	Vladimir Nikulin	[63]
58	Cheng Soon Ong	[40] [48] [56] [61] [64]
59	Takashi Onoda	[17] [24]
60	Zoltán L. Óvári	[26]
61	John C. Platt	[16] [31]
62	Novi Quadrianto	[106] [109]
63	Malte J. Rasch	[75] [76] [98] [102]
64	Gunnar Rätsch	[5] [11] [15] [17] [18] [20] [24] [39] [45]
65	Alistair P. Rendell	[110]
66	Sam T. Roweis	[85]
67	Bernhard Schölkopf	[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [27] [28] [30] [31] [32] [33] [36] [41] [42] [45] [51] [52] [59] [60] [68] [74] [75] [76] [89] [96] [97] [98] [102] [108]
68	Matthias Scholz	[11]
69	Stefan Schönauer	[64]
70	Nicol N. Schraudolph	[73]
71	Tim D. Sears	[72]
72	John Shawe-Taylor	[16] [19] [31]
73	Hao Shen	[77]
74	Qinfeng Shi	[112]
75	Pannagadatta K. Shivaswamy	[55] [71]
76	Patrice Y. Simard (Patrice Simard)	[3]
77	Le Song	[84] [86] [89] [92] [93] [94] [96] [97] [105] [106] [108]
78	Ichiro Takeuchi	[72]
79	Choon Hui Teo	[85] [89] [91] [104]
80	Vladimir Vapnik	[1] [2] [3] [5]
81	René Vidal	[82]
82	S. V. N. Vishwanathan (Vishy Vishwanathan)	[38] [46] [47] [54] [58] [62] [64] [66] [69] [73] [82] [88] [91]
83	Li Wang	[112]
84	Markus Weimer	[87] [99] [103] [111]
85	Kilian Weinberger	[113]
86	Jason Weston	[18] [45]
87	Robert C. Williamson	[12] [16] [19] [21] [22] [23] [26] [30] [31] [32] [33] [34] [35] [40] [44] [61]
88	Ahmed El Zein	[110]
89	Xinhua Zhang	[105] [108]
90	Alexander Zien	[20]