Alexander J. Smola

Alex J. Smola

	2009
117		Le Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu: Hilbert space embeddings of conditional distributions with applications to dynamical systems. ICML 2009: 121
116		Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg: Feature hashing for large scale multitask learning. ICML 2009: 140
115		Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt: Near-optimal Supervised Feature Selection among Frequent Subgraphs. SDM 2009: 1075-1086
114		Kilian Q. Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alex J. Smola: Feature Hashing for Large Scale Multitask Learning CoRR abs/0902.2206: (2009)
113		Tibério S. Caetano, Julian John McAuley, Li Cheng, Quoc V. Le, Alexander J. Smola: Learning Graph Matching. IEEE Trans. Pattern Anal. Mach. Intell. 31(6): 1048-1058 (2009)
	2008
112		Qinfeng Shi, Li Wang, Li Cheng, Alexander J. Smola: Discriminative human action segmentation and recognition using semi-Markov model. CVPR 2008
111		Markus Weimer, Alexandros Karatzoglou, Alex J. Smola: Improving Maximum Margin Matrix Factorization. ECML/PKDD (1) 2008: 14
110		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		Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le: Estimating labels from label proportions. ICML 2008: 776-783
108		Le Song, Xinhua Zhang, Alex J. Smola, Arthur Gretton, Bernhard Schölkopf: Tailoring density estimation via reproducing kernel moment matching. ICML 2008: 992-999
107		Julian John McAuley, Tibério S. Caetano, Alexander J. Smola: Robust Near-Isometric Matching via Structured Learning of Graphical Models. NIPS 2008: 1057-1064
106		Novi Quadrianto, Le Song, Alex J. Smola: Kernelized Sorting. NIPS 2008: 1289-1296
105		Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola: Kernel Measures of Independence for non-iid Data. NIPS 2008: 1937-1944
104		Olivier Chapelle, Chuong B. Do, Quoc V. Le, Alexander J. Smola, Choon Hui Teo: Tighter Bounds for Structured Estimation. NIPS 2008: 281-288
103		Markus Weimer, Alexandros Karatzoglou, Alex J. Smola: Adaptive collaborative filtering. RecSys 2008: 275-282
102		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		Tibério S. Caetano, Julian John McAuley, Li Cheng, Quoc V. Le, Alex J. Smola: Learning Graph Matching CoRR abs/0806.2890: (2008)
100		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		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		Alex J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf: A Hilbert Space Embedding for Distributions. ALT 2007: 13-31
96		Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf: A Hilbert Space Embedding for Distributions. Discovery Science 2007: 40-41
95		Tibério S. Caetano, Li Cheng, Quoc V. Le, Alex J. Smola: Learning Graph Matching. ICCV 2007: 1-8
94		Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt: A dependence maximization view of clustering. ICML 2007: 815-822
93		Le Song, Alex J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo: Supervised feature selection via dependence estimation. ICML 2007: 823-830
92		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		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		Alex J. Smola: Learning Graph Matching. MLG 2007
89		Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alex J. Smola: A Kernel Statistical Test of Independence. NIPS 2007
88		Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le: Bundle Methods for Machine Learning. NIPS 2007
87		Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alex J. Smola: COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . NIPS 2007
86		Le Song, Alex J. Smola, Karsten M. Borgwardt, Arthur Gretton: Colored Maximum Variance Unfolding. NIPS 2007
85		Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola: Convex Learning with Invariances. NIPS 2007
84		Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo: Supervised Feature Selection via Dependence Estimation CoRR abs/0704.2668: (2007)
83		Quoc V. Le, Alexander J. Smola: Direct Optimization of Ranking Measures CoRR abs/0704.3359: (2007)
82		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		Yasemin Altun, Alexander J. Smola: Unifying Divergence Minimization and Statistical Inference Via Convex Duality. COLT 2006: 139-153
80		Quoc V. Le, Alexander J. Smola, Thomas Gärtner, Yasemin Altun: Transductive Gaussian Process Regression with Automatic Model Selection. ECML 2006: 306-317
79		Quoc V. Le, Alex J. Smola, Thomas Gärtner: Simpler knowledge-based support vector machines. ICML 2006: 521-528
78		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		Hao Shen, Knut Hüper, Alexander J. Smola: Newton-Like Methods for Nonparametric Independent Component Analysis. ICONIP (1) 2006: 1068-1077
76		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		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		Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf: Correcting Sample Selection Bias by Unlabeled Data. NIPS 2006: 601-608
73		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		Ichiro Takeuchi, Quoc V. Le, Tim D. Sears, Alexander J. Smola: Nonparametric Quantile Estimation. Journal of Machine Learning Research 7: 1231-1264 (2006)
71		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		Stéphane Canu, Alexander J. Smola: Kernel methods and the exponential family. Neurocomputing 69(7-9): 714-720 (2006)
69		S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, Alexander J. Smola: Kernel extrapolation. Neurocomputing 69(7-9): 721-729 (2006)
	2005
68		Arthur Gretton, Olivier Bousquet, Alex J. Smola, Bernhard Schölkopf: Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT 2005: 63-77
67		Stéphane Canu, Alexander J. Smola: Kernel methods and the exponential family. ESANN 2005: 447-454
66		Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, Alexander J. Smola: Joint Regularization. ESANN 2005: 455-460
65		Quoc V. Le, Alexander J. Smola, Stéphane Canu: Heteroscedastic Gaussian process regression. ICML 2005: 489-496
64		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		Vladimir Nikulin, Alex J. Smola: Universal Clustering with Regularization in Probabilistic Space. MLDM 2005: 142-152
62		Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, S. V. N. Vishwanathan: Large-Scale Multiclass Transduction. NIPS 2005
61		Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Learning the Kernel with Hyperkernels. Journal of Machine Learning Research 6: 1043-1071 (2005)
60		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		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		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		Yasemin Altun, Thomas Hofmann, Alex J. Smola: Gaussian process classification for segmenting and annotating sequences. ICML 2004
56		Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola: Learning with non-positive kernels. ICML 2004
55		Chiranjib Bhattacharyya, Pannagadatta K. Shivaswamy, Alex J. Smola: A Second Order Cone programming Formulation for Classifying Missing Data. NIPS 2004
54		S. V. N. Vishwanathan, Alex J. Smola: Binet-Cauchy Kernels. NIPS 2004
53		Yasemin Altun, Alexander J. Smola, Thomas Hofmann: Exponential Families for Conditional Random Fields. UAI 2004: 2-9
52		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		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		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		Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin: Laplace Propagation. NIPS 2003
45		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		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		Bernhard Schölkopf, Alex J. Smola: A Short Introduction to Learning with Kernels. Machine Learning Summer School 2002: 41-64
41		Alex J. Smola, Bernhard Schölkopf: Bayesian Kernel Methods. Machine Learning Summer School 2002: 65-117
40		Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson: Hyperkernels. NIPS 2002: 478-485
39		Gunnar Rätsch, Alexander J. Smola, Sebastian Mika: Adapting Codes and Embeddings for Polychotomies. NIPS 2002: 513-520
38		S. V. N. Vishwanathan, Alexander J. Smola: Fast Kernels for String and Tree Matching. NIPS 2002: 569-576
37		Glenn Fung, Olvi L. Mangasarian, Alex J. Smola: Minimal Kernel Classifiers. Journal of Machine Learning Research 3: 303-321 (2002)
	2001
36		Bernhard Schölkopf, Ralf Herbrich, Alex J. Smola: A Generalized Representer Theorem. COLT/EuroCOLT 2001: 416-426
35		Adam Kowalczyk, Alex J. Smola, Robert C. Williamson: Kernel Machines and Boolean Functions. NIPS 2001: 439-446
34		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		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		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		Alex J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf: Regularized Principal Manifolds. EuroCOLT 1999: 214-229
21		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		Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson: The Entropy Regularization Information Criterion. NIPS 1999: 342-348
18		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		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		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		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		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		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		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		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		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		Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik: Support Vector Regression Machines. NIPS 1996: 155-161
1		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	[114] [116]
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] [115]
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] [113]
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	Hong Cheng	[115]
17	Li Cheng	[95] [101] [112] [113]
18	Nello Cristianini	[29]
19	Anirban Dasgupta	[114] [116]
20	Chuong B. Do	[104]
21	Harris Drucker	[2]
22	Eleazar Eskin	[46]
23	Peter A. Flach	[43]
24	Matthias O. Franz	[78]
25	Thilo-Thomas Frieß	[10]
26	Kenji Fukumizu	[89] [117]
27	Glenn Fung	[37]
28	Thomas Gärtner	[43] [62] [79] [80]
29	Amir Globerson	[85]
30	Steven E. Golowich	[1]
31	Arthur Gretton	[60] [68] [74] [75] [76] [84] [86] [89] [92] [93] [94] [96] [97] [98] [102] [105] [108] [115]
32	Omri Guttman	[66] [69]
33	Jiawei Han	[115]
34	Ralf Herbrich	[36] [60]
35	Thomas Hofmann	[53] [57]
36	Jiayuan Huang	[74]
37	Jonathan Huang	[117]
38	Knut Hüper	[77]
39	Alexandros Karatzoglou	[87] [99] [103] [111]
40	Linda Kaufman	[2]
41	Jyrki Kivinen	[34] [44]
42	Phil Knirsch	[13] [15]
43	Jens Kohlmorgen	[5]
44	Risi Imre Kondor	[49]
45	Adam Kowalczyk	[35] [43]
46	Hans-Peter Kriegel	[64] [76] [115]
47	John Langford	[114] [116]
48	Quoc V. Le	[62] [65] [72] [79] [80] [83] [87] [88] [91] [95] [101] [104] [109] [113]
49	Christian Lemmen	[20]
50	Thomas Lengauer	[20]
51	Gaëlle Loosli	[58]
52	Olvi L. Mangasarian (O. L. Mangasarian)	[37]
53	Xavier Mary	[56]
54	Julian John McAuley	[78] [100] [101] [107] [113]
55	Eric McCreath	[110]
56	Shahar Mendelson	[50]
57	Sebastian Mika	[11] [15] [17] [18] [20] [22] [24] [32] [39] [45]
58	Klaus-Robert Müller	[5] [6] [7] [8] [11] [14] [15] [17] [18] [20] [24] [45]
59	M. Narasimha Murty	[47]
60	Vladimir Nikulin	[63]
61	Cheng Soon Ong	[40] [48] [56] [61] [64]
62	Takashi Onoda	[17] [24]
63	Zoltán L. Óvári	[26]
64	John C. Platt	[16] [31]
65	Novi Quadrianto	[106] [109]
66	Malte J. Rasch	[75] [76] [98] [102]
67	Gunnar Rätsch	[5] [11] [15] [17] [18] [20] [24] [39] [45]
68	Alistair P. Rendell	[110]
69	Sam T. Roweis	[85]
70	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]
71	Matthias Scholz	[11]
72	Stefan Schönauer	[64]
73	Nicol N. Schraudolph	[73]
74	Tim D. Sears	[72]
75	John Shawe-Taylor	[16] [19] [31]
76	Hao Shen	[77]
77	Qinfeng Shi	[112]
78	Pannagadatta K. Shivaswamy	[55] [71]
79	Patrice Y. Simard (Patrice Simard)	[3]
80	Le Song	[84] [86] [89] [92] [93] [94] [96] [97] [105] [106] [108] [115] [117]
81	Ichiro Takeuchi	[72]
82	Choon Hui Teo	[85] [89] [91] [104]
83	Marisa Thoma	[115]
84	Vladimir Vapnik	[1] [2] [3] [5]
85	René Vidal	[82]
86	S. V. N. Vishwanathan (Vishy Vishwanathan)	[38] [46] [47] [54] [58] [62] [64] [66] [69] [73] [82] [88] [91]
87	Li Wang	[112]
88	Markus Weimer	[87] [99] [103] [111]
89	Kilian Q. Weinberger	[114] [116]
90	Jason Weston	[18] [45]
91	Robert C. Williamson	[12] [16] [19] [21] [22] [23] [26] [30] [31] [32] [33] [34] [35] [40] [44] [61]
92	Xifeng Yan	[115]
93	Philip S. Yu	[115]
94	Ahmed El Zein	[110]
95	Xinhua Zhang	[105] [108]
96	Alexander Zien	[20]

Colors in the list of coauthors