Alexander J. Smola

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2011
149Parallel Online Learning. Daniel Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola. CoRR (abs/1103.4204) (2011). Web SearchBibTeXDownload
148Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models. Qinfeng Shi, Li Cheng, Li Wang, Alex J. Smola. International Journal of Computer Vision (93): 22-32 (2011). Web SearchBibTeXDownload
147Linear-Time Estimators for Propensity Scores. Deepak Agarwal, Lihong Li, Alexander J. Smola. Journal of Machine Learning Research - Proceedings Track (15): 93-100 (2011). Web SearchBibTeXDownload
146Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text. Amr Ahmed, Qirong Ho, Choon Hui Teo, Jacob Eisenstein, Alexander J. Smola, Eric P. Xing. Journal of Machine Learning Research - Proceedings Track (15): 101-109 (2011). Web SearchBibTeXDownload
145Scalable distributed inference of dynamic user interests for behavioral targeting. Amr Ahmed, Yucheng Low, Mohamed Aly, Vanja Josifovski, Alexander J. Smola. KDD 2011, 114-122. Web SearchBibTeXDownload
144Multiple domain user personalization. Yucheng Low, Deepak Agarwal, Alexander J. Smola. KDD 2011, 123-131. Web SearchBibTeXDownload
143Guest editorial: model selection and optimization in machine learning. Süreyya Özögür-Akyüz, Devrim Ünay, Alexander J. Smola. Machine Learning (85): 1-2 (2011). Web SearchBibTeXDownload
142Collaborative competitive filtering: learning recommender using context of user choice. Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hongyuan Zha, Zhaohui Zheng. SIGIR 2011, 295-304. Web SearchBibTeXDownload
141Bid generation for advanced match in sponsored search. Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josifovski, George Mavromatis, Alex J. Smola. WSDM 2011, 515-524. Web SearchBibTeXDownload
140Scalable clustering of news search results. Srinivas Vadrevu, Choon Hui Teo, Suju Rajan, Kunal Punera, Byron Dom, Alexander J. Smola, Yi Chang, Zhaohui Zheng. WSDM 2011, 675-684. Web SearchBibTeXDownload
139Like like alike: joint friendship and interest propagation in social networks. Shuang-Hong Yang, Bo Long, Alexander J. Smola, Narayanan Sadagopan, Zhaohui Zheng, Hongyuan Zha. WWW 2011, 537-546. Web SearchBibTeXDownload
138Unified analysis of streaming news. Amr Ahmed, Qirong Ho, Jacob Eisenstein, Eric P. Xing, Alexander J. Smola, Choon Hui Teo. WWW 2011, 267-276. Web SearchBibTeXDownload
137WWW 2011 invited tutorial overview: latent variable models on the internet. Amr Ahmed, Alexander J. Smola. WWW (Companion Volume) 2011, 281-282. Web SearchBibTeXDownload
2010
136Distributed Flow Algorithms for Scalable Similarity Visualization. Novi Quadrianto, Dale Schuurmans, Alex J. Smola. ICDM Workshops 2010, 1220-1227. Web SearchBibTeXDownload
135Hilbert Space Embeddings of Hidden Markov Models. Le Song, Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon, Alexander J. Smola. ICML 2010, 991-998. Web SearchBibTeXDownload
134Kernelized Sorting. Novi Quadrianto, Alex J. Smola, Le Song, Tinne Tuytelaars. IEEE Trans. Pattern Anal. Mach. Intell. (32): 1809-1821 (2010). Web SearchBibTeXDownload
133Bundle Methods for Regularized Risk Minimization. Choon Hui Teo, S. V. N. Vishwanathan, Alex J. Smola, Quoc V. Le. Journal of Machine Learning Research (11): 311-365 (2010). Web SearchBibTeXDownload
132Collaborative Filtering on a Budget. Alexandros Karatzoglou, Alexander J. Smola, Markus Weimer. Journal of Machine Learning Research - Proceedings Track (9): 389-396 (2010). Web SearchBibTeXDownload
131Word Features for Latent Dirichlet Allocation. James Petterson, Alexander J. Smola, Tibério S. Caetano, Wray L. Buntine, Shravan Narayanamurthy. NIPS 2010, 1921-1929. Web SearchBibTeXDownload
130Multitask Learning without Label Correspondences. Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, S. V. N. Vishwanathan, James Petterson. NIPS 2010, 1957-1965. Web SearchBibTeXDownload
129Optimal Web-Scale Tiering as a Flow Problem. Gilbert Leung, Novi Quadrianto, Alexander J. Smola, Kostas Tsioutsiouliklis. NIPS 2010, 1333-1341. Web SearchBibTeXDownload
128Parallelized Stochastic Gradient Descent. Martin Zinkevich, Markus Weimer, Alexander J. Smola, Lihong Li. NIPS 2010, 2595-2603. Web SearchBibTeXDownload
127Wearable sensor activity analysis using semi-Markov models with a grammar. O. Thomas, Peter Sunehag, Gideon Dror, S. Yun, S. Kim, Matthew W. Robards, Alexander J. Smola, D. Green, P. Saunders. Pervasive and Mobile Computing (6): 342-350 (2010). Web SearchBibTeXDownload
126An Architecture for Parallel Topic Models. Alexander J. Smola, Shravan Narayanamurthy. PVLDB (3): 703-710 (2010). Web SearchBibTeXDownload
125Discriminative frequent subgraph mining with optimality guarantees. Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt. Statistical Analysis and Data Mining (3): 302-318 (2010). Web SearchBibTeXDownload
124Super-Samples from Kernel Herding. Yutian Chen, Max Welling, Alex J. Smola. UAI 2010, 109-116. Web SearchBibTeXDownload
123IntervalRank: isotonic regression with listwise and pairwise constraints. Taesup Moon, Alex J. Smola, Yi Chang, Zhaohui Zheng. WSDM 2010, 151-160. Web SearchBibTeXDownload
2009
122Feature Hashing for Large Scale Multitask Learning. Kilian Q. Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alex J. Smola. CoRR (abs/0902.2206) (2009). Web SearchBibTeXDownload
121Feature hashing for large scale multitask learning. Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg. ICML 2009, 140. Web SearchBibTeXDownload
120Hilbert space embeddings of conditional distributions with applications to dynamical systems. Le Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu. ICML 2009, 121. Web SearchBibTeXDownload
119Learning Graph Matching. Tibério S. Caetano, Julian John McAuley, Li Cheng, Quoc V. Le, Alexander J. Smola. IEEE Trans. Pattern Anal. Mach. Intell. (31): 1048-1058 (2009). Web SearchBibTeXDownload
118Hash Kernels for Structured Data. Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, S. V. N. Vishwanathan. Journal of Machine Learning Research (10): 2615-2637 (2009). Web SearchBibTeXDownload
117Estimating Labels from Label Proportions. Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le. Journal of Machine Learning Research (10): 2349-2374 (2009). Web SearchBibTeXDownload
116Relative Novelty Detection. Alexander J. Smola, Le Song, Choon Hui Teo. Journal of Machine Learning Research - Proceedings Track (5): 536-543 (2009). Web SearchBibTeXDownload
115Hash Kernels. Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, Alexander L. Strehl, Vishy Vishwanathan. Journal of Machine Learning Research - Proceedings Track (5): 496-503 (2009). Web SearchBibTeXDownload
114Distribution Matching for Transduction. Novi Quadrianto, James Petterson, Alex J. Smola. NIPS 2009, 1500-1508. Web SearchBibTeXDownload
113Slow Learners are Fast. Martin Zinkevich, Alex J. Smola, John Langford. NIPS 2009, 2331-2339. Web SearchBibTeXDownload
112Near-optimal Supervised Feature Selection among Frequent Subgraphs. Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt. SDM 2009, 1075-1086. Cited by 5Web SearchBibTeXDownload
2008
111Learning Graph Matching. Tibério S. Caetano, Julian John McAuley, Li Cheng, Quoc V. Le, Alexander J. Smola. CoRR (abs/0806.2890) (2008). Web SearchBibTeXDownload
110A Kernel Method for the Two-Sample Problem. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola. CoRR (abs/0805.2368) (2008). Web SearchBibTeXDownload
109Discriminative human action segmentation and recognition using semi-Markov model. Qinfeng Shi, Li Wang, Li Cheng, Alexander J. Smola. CVPR 2008. Web SearchBibTeXDownload
108Improving Maximum Margin Matrix Factorization. Markus Weimer, Alexandros Karatzoglou, Alex J. Smola. ECML/PKDD (1) 2008, 14. Web SearchBibTeXDownload
107Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning. Ahmed H. El Zein, Eric McCreath, Alistair P. Rendell, Alex J. Smola. ICCS (1) 2008, 466-475. Web SearchBibTeXDownload
106Tailoring density estimation via reproducing kernel moment matching. Le Song, Xinhua Zhang, Alex J. Smola, Arthur Gretton, Bernhard Schölkopf. ICML 2008, 992-999. Web SearchBibTeXDownload
105Estimating labels from label proportions. Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le. ICML 2008, 776-783. Web SearchBibTeXDownload
104Improving maximum margin matrix factorization. Markus Weimer, Alexandros Karatzoglou, Alex J. Smola. Machine Learning (72): 263-276 (2008). Web SearchBibTeXDownload
103Robust Near-Isometric Matching via Structured Learning of Graphical Models. Julian John McAuley, Tibério S. Caetano, Alexander J. Smola. NIPS 2008, 1057-1064. Web SearchBibTeXDownload
102Kernelized Sorting. Novi Quadrianto, Alex J. Smola, Le Song, Tinne Tuytelaars. NIPS 2008, 1289-1296. Web SearchBibTeXDownload
101Kernel Measures of Independence for non-iid Data. Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola. NIPS 2008, 1937-1944. Web SearchBibTeXDownload
100Tighter Bounds for Structured Estimation. Olivier Chapelle, Chuong B. Do, Quoc V. Le, Alexander J. Smola, Choon Hui Teo. NIPS 2008, 281-288. Web SearchBibTeXDownload
99Adaptive collaborative filtering. Markus Weimer, Alexandros Karatzoglou, Alex J. Smola. RecSys 2008, 275-282. Web SearchBibTeXDownload
2007
98A Kernel Approach to Comparing Distributions. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola. AAAI 2007, 1637-1641. Web SearchBibTeX
97A Hilbert Space Embedding for Distributions. Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf. ALT 2007, 13-31. Web SearchBibTeXDownload
96Direct Optimization of Ranking Measures. Quoc V. Le, Alexander J. Smola. CoRR (abs/0704.3359) (2007). Web SearchBibTeXDownload
95Supervised Feature Selection via Dependence Estimation. Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo. CoRR (abs/0704.2668) (2007). Web SearchBibTeXDownload
94A Hilbert Space Embedding for Distributions. Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf. Discovery Science 2007, 40-41. Web SearchBibTeXDownload
93Semi-Markov Models for Sequence Segmentation. Qinfeng Shi, Yasemin Altun, Alex J. Smola, S. V. N. Vishwanathan. EMNLP-CoNLL 2007, 640-648. Web SearchBibTeXDownload
92Learning Graph Matching. Tibério S. Caetano, Julian John McAuley, Li Cheng, Quoc V. Le, Alexander J. Smola. ICCV 2007, 1-8. Web SearchBibTeXDownload
91A dependence maximization view of clustering. Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt. ICML 2007, 815-822. Web SearchBibTeXDownload
90Supervised feature selection via dependence estimation. Le Song, Alex J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo. ICML 2007, 823-830. Web SearchBibTeXDownload
89Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes. S. V. N. Vishwanathan, Alexander J. Smola, René Vidal. International Journal of Computer Vision (73): 95-119 (2007). Web SearchBibTeXDownload
88Gene selection via the BAHSIC family of algorithms. Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alexander J. Smola. ISMB/ECCB (Supplement of Bioinformatics) 2007, 490-498. Web SearchBibTeXDownload
87The Need for Open Source Software in Machine Learning. Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson. Journal of Machine Learning Research (8): 2443-2466 (2007). Web SearchBibTeXDownload
86A scalable modular convex solver for regularized risk minimization. Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le. KDD 2007, 727-736. Web SearchBibTeXDownload
85Colored Maximum Variance Unfolding. Le Song, Alex J. Smola, Karsten M. Borgwardt, Arthur Gretton. NIPS 2007. Web SearchBibTeXDownload
84Bundle Methods for Machine Learning. Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le. NIPS 2007. Web SearchBibTeXDownload
83Convex Learning with Invariances. Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola. NIPS 2007. Web SearchBibTeXDownload
82COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alex J. Smola. NIPS 2007. Web SearchBibTeXDownload
81A Kernel Statistical Test of Independence. Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alex J. Smola. NIPS 2007. Web SearchBibTeXDownload
2006
80Unifying Divergence Minimization and Statistical Inference Via Convex Duality. Yasemin Altun, Alexander J. Smola. COLT 2006, 139-153. Web SearchBibTeXDownload
79Transductive Gaussian Process Regression with Automatic Model Selection. Quoc V. Le, Alexander J. Smola, Thomas Gärtner, Yasemin Altun. ECML 2006, 306-317. Web SearchBibTeXDownload
78Simpler knowledge-based support vector machines. Quoc V. Le, Alex J. Smola, Thomas Gärtner. ICML 2006, 521-528. Web SearchBibTeXDownload
77Learning high-order MRF priors of color images. Julian John McAuley, Tibério S. Caetano, Alex J. Smola, Matthias O. Franz. ICML 2006, 617-624. Web SearchBibTeXDownload
76Newton-Like Methods for Nonparametric Independent Component Analysis. Hao Shen, Knut Hüper, Alexander J. Smola. ICONIP (1) 2006, 1068-1077. Web SearchBibTeXDownload
75Integrating structured biological data by Kernel Maximum Mean Discrepancy. Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola. ISMB (Supplement of Bioinformatics) 2006, 49-57. Cited by 41Web SearchBibTeXDownload
74Second Order Cone Programming Approaches for Handling Missing and Uncertain Data. Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola. Journal of Machine Learning Research (7): 1283-1314 (2006). Web SearchBibTeXDownload
73Step Size Adaptation in Reproducing Kernel Hilbert Space. S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola. Journal of Machine Learning Research (7): 1107-1133 (2006). Web SearchBibTeXDownload
72Nonparametric Quantile Estimation. Ichiro Takeuchi, Quoc V. Le, Tim D. Sears, Alexander J. Smola. Journal of Machine Learning Research (7): 1231-1264 (2006). Web SearchBibTeXDownload
71Kernel methods and the exponential family. Stéphane Canu, Alexander J. Smola. Neurocomputing (69): 714-720 (2006). Web SearchBibTeXDownload
70Kernel extrapolation. S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, Alexander J. Smola. Neurocomputing (69): 721-729 (2006). Web SearchBibTeXDownload
69Correcting Sample Selection Bias by Unlabeled Data. Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf. NIPS 2006, 601-608. Web SearchBibTeXDownload
68A Kernel Method for the Two-Sample-Problem. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola. NIPS 2006, 513-520. Web SearchBibTeXDownload
2005
67Measuring Statistical Dependence with Hilbert-Schmidt Norms. Arthur Gretton, Olivier Bousquet, Alex J. Smola, Bernhard Schölkopf. ALT 2005, 63-77. Web SearchBibTeXDownload
66Joint Regularization. Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, Alexander J. Smola. ESANN 2005, 455-460. Web SearchBibTeXDownload
65Kernel methods and the exponential family. Stéphane Canu, Alexander J. Smola. ESANN 2005, 447-454. Web SearchBibTeXDownload
64Heteroscedastic Gaussian process regression. Quoc V. Le, Alexander J. Smola, Stéphane Canu. ICML 2005, 489-496. Web SearchBibTeXDownload
63Protein function prediction via graph kernels. Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, Hans-Peter Kriegel. ISMB (Supplement of Bioinformatics) 2005, 47-56. Cited by 90Web SearchBibTeXDownload
62Learning the Kernel with Hyperkernels. Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson. Journal of Machine Learning Research (6): 1043-1071 (2005). Web SearchBibTeXDownload
61Kernel Methods for Measuring Independence. Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf. Journal of Machine Learning Research (6): 2075-2129 (2005). Web SearchBibTeXDownload
60Universal Clustering with Regularization in Probabilistic Space. Vladimir Nikulin, Alex J. Smola. MLDM 2005, 142-152. Web SearchBibTeXDownload
59Experimentally optimal nu in support vector regression for different noise models and parameter settings. Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola. Neural Networks (18): 205 (2005). Web SearchBibTeXDownload
58Large-Scale Multiclass Transduction. Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, S. V. N. Vishwanathan. NIPS 2005. Web SearchBibTeXDownload
57Boîte à outils SVM simple et rapide. Gaëlle Loosli, Stéphane Canu, S. V. N. Vishwanathan, Alexander J. Smola, M. Chattopadhyay. Revue d'Intelligence Artificielle (19): 741-767 (2005). Web SearchBibTeXDownload
2004
56Learning with non-positive kernels. Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola. ICML 2004. Web SearchBibTeXDownload
55Gaussian process classification for segmenting and annotating sequences. Yasemin Altun, Thomas Hofmann, Alex J. Smola. ICML 2004. Web SearchBibTeXDownload
54Online learning with kernels. Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson. IEEE Transactions on Signal Processing (52): 2165-2176 (2004). Web SearchBibTeXDownload
53Experimentally optimal v in support vector regression for different noise models and parameter settings. Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola. Neural Networks (17): 127-141 (2004). Web SearchBibTeXDownload
52Binet-Cauchy Kernels. S. V. N. Vishwanathan, Alex J. Smola. NIPS 2004. Web SearchBibTeXDownload
51A Second Order Cone programming Formulation for Classifying Missing Data. Chiranjib Bhattacharyya, Pannagadatta K. Shivaswamy, Alex J. Smola. NIPS 2004. Web SearchBibTeXDownload
50A tutorial on support vector regression. Alexander J. Smola, Bernhard Schölkopf. Statistics and Computing (14): 199-222 (2004). Web SearchBibTeXDownload
49Exponential Families for Conditional Random Fields. Yasemin Altun, Alexander J. Smola, Thomas Hofmann. UAI 2004, 2-9. Web SearchBibTeXDownload
2003
48Kernels and Regularization on Graphs. Alex J. Smola, Risi Imre Kondor. COLT 2003, 144-158. Web SearchBibTeXDownload
47Machine Learning with Hyperkernels. Cheng Soon Ong, Alex J. Smola. ICML 2003, 568-575. Web SearchBibTeX
46Classification in a normalized feature space using support vector machines. Arnulf B. A. Graf, Alexander J. Smola, Silvio Borer. IEEE Transactions on Neural Networks (14): 597-605 (2003). Web SearchBibTeXDownload
45Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller. IEEE Trans. Pattern Anal. Mach. Intell. (25): 623-633 (2003). Web SearchBibTeXDownload
44Laplace Propagation. Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin. NIPS 2003. Web SearchBibTeXDownload
2002
43Large Margin Classification for Moving Targets. Jyrki Kivinen, Alex J. Smola, Robert C. Williamson. ALT 2002, 113-127. Web SearchBibTeXDownload
42Multi-Instance Kernels. Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alex J. Smola. ICML 2002, 179-186. Web SearchBibTeX
41Minimal Kernel Classifiers. Glenn Fung, Olvi L. Mangasarian, Alex J. Smola. Journal of Machine Learning Research (3): 303-321 (2002). Web SearchBibTeXDownload
40Bayesian Kernel Methods. Alex J. Smola, Bernhard Schölkopf. Machine Learning Summer School 2002, 65-117. Web SearchBibTeXDownload
39A Short Introduction to Learning with Kernels. Bernhard Schölkopf, Alex J. Smola. Machine Learning Summer School 2002, 41-64. Web SearchBibTeXDownload
38Fast Kernels for String and Tree Matching. S. V. N. Vishwanathan, Alexander J. Smola. NIPS 2002, 569-576. Web SearchBibTeXDownload
37Adapting Codes and Embeddings for Polychotomies. Gunnar Rätsch, Alexander J. Smola, Sebastian Mika. NIPS 2002, 513-520. Web SearchBibTeXDownload
2001
36A Generalized Representer Theorem. Bernhard Schölkopf, Ralf Herbrich, Alex J. Smola. COLT/EuroCOLT 2001, 416-426. Web SearchBibTeXDownload
35Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf. IEEE Transactions on Information Theory (47): 2516-2532 (2001). Web SearchBibTeXDownload
34Regularized Principal Manifolds. Alex J. Smola, Robert C. Williamson, Bernhard Schölkopf, Robert C. Williamson. Journal of Machine Learning Research (1): 179-209 (2001). Web SearchBibTeXDownload
33Estimating the Support of a High-Dimensional Distribution. Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, Robert C. Williamson. Neural Computation (13): 1443-1471 (2001). Web SearchBibTeXDownload
32Kernel Machines and Boolean Functions. Adam Kowalczyk, Alex J. Smola, Robert C. Williamson. NIPS 2001, 439-446. Web SearchBibTeXDownload
31Online Learning with Kernels. Jyrki Kivinen, Alex J. Smola, Robert C. Williamson. NIPS 2001, 785-792. Web SearchBibTeXDownload
2000
30Entropy Numbers of Linear Function Classes. Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf. COLT 2000, 309-319. Web SearchBibTeX
29Sparse Greedy Matrix Approximation for Machine Learning. Alex J. Smola, Bernhard Schölkopf. ICML 2000, 911-918. Web SearchBibTeX
28Query Learning with Large Margin Classifiers. Colin Campbell, Nello Cristianini, Alex J. Smola. ICML 2000, 111-118. Web SearchBibTeX
27Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments. Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola. IJCNN (5) 2000, 199-204. Web SearchBibTeXDownload
26New Support Vector Algorithms. Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett. Neural Computation (12): 1207-1245 (2000). Web SearchBibTeXDownload
25Regularization with Dot-Product Kernels. Alex J. Smola, Zoltán L. Óvári, Robert C. Williamson. NIPS 2000, 308-314. Web SearchBibTeX
24Sparse Greedy Gaussian Process Regression. Alex J. Smola, Peter L. Bartlett. NIPS 2000, 619-625. Web SearchBibTeX
23Robust Ensemble Learning for Data Mining. Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller. PAKDD 2000, 341-344. Web SearchBibTeXDownload
1999
22Entropy Numbers, Operators and Support Vector Kernels. Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf. EuroCOLT 1999, 285-299. Web SearchBibTeXDownload
21Regularized Principal Manifolds. Alex J. Smola, Robert C. Williamson, Bernhard Schölkopf, Robert C. Williamson. EuroCOLT 1999, 214-229. Web SearchBibTeXDownload
20Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites. Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Christian Lemmen, Alex J. Smola, Thomas Lengauer, Klaus-Robert Müller. German Conference on Bioinformatics 1999, 37-43. Web SearchBibTeX
19Input space versus feature space in kernel-based methods. Bernhard Schölkopf, Sebastian Mika, Christopher J. C. Burges, Phil Knirsch, Klaus-Robert Müller, Gunnar Rätsch, Alexander J. Smola. IEEE Transactions on Neural Networks (10): 1000-1017 (1999). Web SearchBibTeXDownload
18Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. Bernhard Schölkopf, Klaus-Robert Müller, Alex J. Smola. Inform., Forsch. Entwickl. (14): 154-163 (1999). Web SearchBibTeXDownload
17Support Vector Method for Novelty Detection. Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt. NIPS 1999, 582-588. Web SearchBibTeXDownload
16The Entropy Regularization Information Criterion. Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson. NIPS 1999, 342-348. Web SearchBibTeXDownload
15Invariant Feature Extraction and Classification in Kernel Spaces. Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller. NIPS 1999, 526-532. Web SearchBibTeXDownload
14v-Arc: Ensemble Learning in the Presence of Outliers. Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika. NIPS 1999, 561-567. Web SearchBibTeXDownload
1998
13On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion. Alex J. Smola, Bernhard Schölkopf. Algorithmica (22): 211-231 (1998). Web SearchBibTeXDownload
12Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces. Bernhard Schölkopf, Alex J. Smola, Phil Knirsch, Chris Burges. DAGM-Symposium 1998, 125-132. Web SearchBibTeX
11Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller. Neural Computation (10): 1299-1319 (1998). Web SearchBibTeXDownload
10The connection between regularization operators and support vector kernels. Alex J. Smola, Bernhard Schölkopf, Klaus-Robert Müller. Neural Networks (11): 637-649 (1998). Web SearchBibTeXDownload
9Shrinking the Tube: A New Support Vector Regression Algorithm. Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson. NIPS 1998, 330-336. Web SearchBibTeXDownload
8Semiparametric Support Vector and Linear Programming Machines. Alex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf. NIPS 1998, 585-591. Web SearchBibTeXDownload
7Kernel PCA and De-Noising in Feature Spaces. Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch. NIPS 1998, 536-542. Web SearchBibTeXDownload
1997
6Predicting Time Series with Support Vector Machines. Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik. ICANN 1997, 999-1004. Web SearchBibTeXDownload
5Kernel Principal Component Analysis. Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller. ICANN 1997, 583-588. Web SearchBibTeXDownload
4Prior Knowledge in Support Vector Kernels. Bernhard Schölkopf, Patrice Simard, Alex J. Smola, Vladimir Vapnik. NIPS 1997. Web SearchBibTeX
3From Regularization Operators to Support Vector Kernels. Alex J. Smola, Bernhard Schölkopf. NIPS 1997. Web SearchBibTeX
1996
2Support Vector Regression Machines. Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik. NIPS 1996, 155-161. Cited by 2Web SearchBibTeXDownload
1Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. Vladimir Vapnik, Steven E. Golowich, Alex J. Smola. NIPS 1996, 281-287. Web SearchBibTeXDownload
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References
1. ^ AAAI-10 – IAAI-10 Organizing and Program Committees - Retrieved 2011-03-19 - details
2. ^ WWW2010 – Raleigh: Committee - Retrieved 2011-03-19 - details
3. ^ Wednesday - Retrieved 2012-01-12 - details
4. ^ Forum for Artificial Intelligence - Retrieved 2012-01-20 - details
5. ^ icml2008@helsinki.fi - Retrieved 2011-06-04 - details
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