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Alex Smola

 

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Alex Smola

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Books (1):

Schölkopf B, Burges CJC and Smola AJ: Advances in Kernel Methods: Support Vector Learning, 352, MIT Press, Cambridge, MA, USA, (1999). ISBN: 0-262-19416-3

Articles (9):

Song L, Smola A, Gretton A, Bedo J and Borgwardt K (May-2012) Feature Selection via Dependence Maximization Journal of Machine Learning Research 13 1393-1434.
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Gretton A, Borgwardt K, Rasch M, Schölkopf B and Smola A (March-2012) A Kernel Two-Sample Test Journal of Machine Learning Research 13 723−773.
Thoma M, Cheng H, Gretton A, Han J, Kriegel H-P, Smola AJ, Song L, Yu PS, Yan X and Borgwardt KM (October-2010) Discriminative frequent subgraph mining with optimality guarantees Statistical Analysis and Data Mining 3(5) 302–318.
Song L, Bedo J, Borgwardt KM, Gretton A and Smola A (July-2007) Gene selection via the BAHSIC family of algorithms Bioinformatics 23(13: ISMB/ECCB 2007 Conference Proceedings) i490-i498.
Borgwardt KM, Gretton A, Rasch M, Kriegel H-P, Schölkopf B and Smola A (August-2006) Integrating Structured Biological data by Kernel Maximum Mean Discrepancy Bioinformatics 22(4: ISMB 2006 Conference Proceedings) e49-e57.
Vishwanathan SVN, Borgwardt KM, Guttman O and Smola AJ (March-2006) Kernel extrapolation Neurocomputing 69(7-9) 721-729.
Borgwardt KM, Ong CS, Schönauer S, Vishwanathan , Smola AJ and Kriegel H-P (June-2005) Protein function prediction via graph kernels Bioinformatics 21(Suppl. 1: ISMB 2005 Proceedings) i47-i56.
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Schölkopf B, Smola AJ, Williamson RC and Bartlett PL (May-2000) New Support Vector Algorithms Neural Computation 12(5) 1207-1245.
Smola AJ, Schölkopf B and Müller K-R (June-1998) The connection between regularization operators and support vector kernels Neural Networks 11(4) 637-649.

Conference papers (40):

Gretton A, Borgwardt KM, Rasch M, Schölkopf B and Smola A (September-2007) A Kernel Method for the Two-Sample-Problem In: Advances in Neural Information Processing Systems 19, , Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), MIT Press, Cambridge, MA, USA, Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 513-520.
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Huang J, Smola A, Gretton A, Borgwardt KM and Schölkopf B (September-2007) Correcting Sample Selection Bias by Unlabeled Data In: Advances in Neural Information Processing Systems 19, , Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), MIT Press, Cambridge, MA, USA, Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 601-608.
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Gretton A, Borgwardt KM, Rasch M, Schölkopf B and Smola AJ (July-2007) A Kernel Approach to Comparing Distributions, Twenty-Second AAAI Conference on Artificial Intelligence (IAAI-07), AAAI Press, Menlo Park, CA, USA, Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07), 1637-1641.
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Song L, Smola AJ, Gretton A and Borgwardt KM (June-2007) A Dependence Maximization View of Clustering, 24th Annual International Conference on Machine Learning (ICML 2007), ACM Press, New York, NY, USA, Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007), 815-822.
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Song L, Smola AJ, Gretton A, Borgwardt KM and Bedo J (June-2007) Supervised Feature Selection via Dependence Estimation, 24th Annual International Conference on Machine Learning (ICML 2007), ACM Press, New York, NY, USA, Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007), 823-830.
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Borgwardt KM, Guttman O, Vishwanathan SVN and Smola AJ (April-2005) Joint Regularization, 13th European Symposium on Artificial Neural Networks (ESANN 2005), D-Side, Evere, Belgium, 455-460.
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Gretton A, Smola AJ, Bousquet O, Herbrich R, Belitski A, Augath M, Murayama Y, Pauls J, Schölkopf B and Logothetis NK (January-2005) Kernel Constrained Covariance for Dependence Measurement, Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS 2005), Society for Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, AISTATS 2005, 112-119.
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Altun Y, Hofmann T and Smola AJ (July-2004) Gaussian Process Classification for Segmenting and Annotating Sequences, Twenty-first International Conference on Machine Learning (ICML 2004), ACM Press, New York, USA, Proceedings of the 21st International Conference on Machine Learning (ICML 2004), 4.
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Ong CS, Mary X, Canu S and Smola AJ (July-2004) Learning with Non-Positive Kernels, Twenty-first International Conference on Machine Learning (ICML 2004), ACM Press, New York, NY, USA, Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), 81.
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Smola AJ, Mangasarian O and Schölkopf B (2002) Sparse kernel feature analysis In: Studies in Classification, Data Analysis, and Knowledge Organization, , 24th Annual Conference of the Gesellschaft für Klassifikation 2000, Springer, Berlin, Germany(99-04), 167-178, Series: Studies in Classification, Data Analysis, and Knowledge Organization.
Schölkopf B, Herbrich R and Smola AJ (July-2001) A Generalized Representer Theorem In: Computational Learning Theory, , 14th Annual Conference on Computational Learning Theory (COLT 2001) and 5th European Conference on Computational Learning Theory (EuroCOLT 2001), Springer, Berlin, Germany, Computational Learning Theory(2111), 416-426, Series: Lecture Notes in Computer Science ; 2111.
Williamson RC, Smola AJ and Schölkopf B (July-2000) Entropy Numbers of Linear Function Classes., 13th Annual Conference on Computational Learning Theory (COLT 2000), Morgan Kaufmann, San Francisco, CA, USA, 309-319.
Smola AJ and Schölkopf B (July-2000) Sparse Greedy Matrix Approximation for Machine Learning, Seventeenth International Conference on Machine Learning (ICML 2000), Morgan Kaufmann, San Francisco, CA, USA, 911-918.
Mika S, Rätsch G, Weston J, Schölkopf B, Smola AJ and Müller K-R (June-2000) Invariant feature extraction and classification in kernel spaces In: Advances in neural information processing systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 526-532.
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Schölkopf B, Williamson RC, Smola AJ, Shawe-Taylor J and Platt JC (June-2000) Support vector method for novelty detection In: Advances in Neural Information Processing Systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 582-588.
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Smola AJ, Shawe-Taylor J, Schölkopf B and Williamson RC (June-2000) The entropy regularization information criterion In: Advances in Neural Information Processing Systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 342-348.
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Rätsch G, Schölkopf B, Smola AJ, Müller K-R, Onoda T and Mika S (June-2000) v-Arc: Ensemble Learning in the Presence of Outliers In: Advances in Neural Information Processing Systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 561-567.
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Rätsch G, Schölkopf B, Smola AJ, Mika S, Onoda T and Müller K-R (April-2000) Robust Ensemble Learning for Data Mining In: Knowledge Discovery and Data Mining: Current Issues and New Applications, , Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000), Springer, Berlin, Germany, 341-344, Series: Lecture Notes in Artificial Intelligence ; 1805.
Smola AJ, Bartlett PJ, Schölkopf B and Schuurmans D (2000) Advances in Large Margin Classifiers, NIPS 1998 Workshop “Advances in Large Margin Classifiers”, MIT Press, Cambridge, MA, USA, 422, Series: Neural Information Processing.
Smola AJ, Elisseeff A, Schölkopf B and Williamson RC (2000) Entropy numbers for convex combinations and MLPs In: Advances in Large Margin Classifiers, , NIPS 1998 Workshop “Advances in Large Margin Classifiers”, MIT Press, Cambridge, MA, USA, 369-387, Series: Neural Information Processing Series.
Oliver N, Schölkopf B and Smola AJ (2000) Natural Regularization from Generative Models In: Advances in Large Margin Classifiers, , NIPS 1998 Workshop “Advances in Large Margin Classifiers”, MIT Press, Cambridge, MA, USA, 51-60, Series: Neural Information Processing Series.
Rätsch G, Schölkopf B, Smola AJ, Mika S, Onoda T and Müller K-R (2000) Robust ensemble learning In: Advances in Large Margin Classifiers, , NIPS 1998 Workshop “Advances in Large Margin Classifiers”, MIT Press, Cambridge, MA, USA, 207-220, Series: Neural Information Processing Series.
Graepel T, Herbrich R, Schölkopf B, Smola AJ, Bartlett P, Müller K, Obermayer K and Williamson RC (September-1999) Classification on proximity data with LP-machines, Ninth International Conference on Artificial Neural Networks (ICANN 99), Institute of Electrical Engineers, London, UK, 304-309, Series: Conference publication of the Institution of Electrical Engineers ; 470.
Schölkopf B, Shawe-Taylor J, Smola AJ and Williamson RC (September-1999) Kernel-dependent support vector error bounds, Ninth International Conference on Artificial Neural Networks (ICANN 99), Institute of Electrical Engineers, London, UK, 103-108, Series: Conference publication of the Institution of Electrical Engineers ; 470.
Smola AJ, Schölkopf B and Rätsch G (September-1999) Linear programs for automatic accuracy control in regression, Ninth International Conference on Artificial Neural Networks (ICANN 99), Institute of Electrical Engineers, London, UK, 575-580, Series: Conference publication of the Institution of Electrical Engineers ; 470.
Vannerem P, Müller K-R, Smola AJ, Schölkopf B and Söldner-Rembold S (April-1999) Classifying LEP data with support vector algorithms, Conference on Artificial Intelligence in High Energy Nuclear Physics (AIHENP '99), 1-7.
Williamson RC, Smola AJ and Schölkopf B (March-1999) Entropy numbers, operators and support vector kernels In: Computational Learning Theory, , 4th European Conference on Computational Learning Theory (EuroCOLT’99), Springer, Berlin, Germany, 285-299, Series: Lecture Notes in Artificial Intelligence ; 1572.
Smola AJ, Williamson RC, Mika S and Schölkopf B (March-1999) Regularized principal manifolds In: Computational Learning Theory, , 4th European Conference on Computational Learning Theory (EuroCOLT’99), Springer, Berlin, Germany, 214-229, Series: Lecture Notes in Artificial Intelligence ; 1572.
Williamson RC, Smola AJ and Schölkopf B (1999) Entropy numbers, operators and support vector kernels. In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, 127-144.
Schölkopf B, Burges CJC and Smola AJ (1999) Introduction to support vector learning In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, 1-15.
Schölkopf B, Smola AJ and Müller K-R (1999) Kernel principal component analysis In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, 327-352.
Schölkopf B, Burges CJC and Smola AJ (1999) Roadmap In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, 17-22.
Müller K-R, Smola AJ, Rätsch G, Schölkopf B, Kohlmorgen J and Vapnik V (1999) Using support vector machines for time series prediction In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, 243-253.
Smola AJ, Murata N, Schölkopf B and Müller K-R (September-1998) Asymptotically Optimal Choice of ε-Loss for Support Vector Machines In: ICANN 98, , 8th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 105-110, Series: Perspectives in Neural Computing.
Smola AJ, Schölkopf B and Müller K-R (September-1998) Convex Cost Functions for Support Vector Regression In: ICANN 98, , 8th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 8th International Conference on Artificial Neural Networks, 99-104, Series: Perspectives in Neural Computing.
Schölkopf B, Mika S, Smola AJ, Rätsch G and Müller K-R (September-1998) Kernel PCA pattern reconstruction via approximate pre-images In: ICANN 98, , 8th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 8th International Conference on Artificial Neural Networks, 147-152, Series: Perspectives in Neural Computing.
Smola AJ and Schölkopf B (June-1998) From regularization operators to support vector kernels In: Advances in Neural Information Processing Systems 10, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, Advances in Neural Information Processing Systems, 343-349, Series: A Bradford Book.
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Schölkopf B, Simard P, Smola AJ and Vapnik V (June-1998) Prior knowledge in support vector kernels In: Advances in Neural Information Processing Systems 10, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, Advances in Neural Information Processing Systems, 640-646, Series: A Bradford Book.
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Smola AJ, Schölkopf B and Müller K-R (February-1998) General cost functions for Support Vector Regression, Ninth Australian Conference on Neural Networks (ACNN'98), University of Queensland, Brisbane, Australia, Ninth Australian Conference on Neural Networks, 79-83.
Schölkopf B, Smola AJ, Müller K-R, Burges C and Vapnik V (February-1998) Support Vector methods in learning and feature extraction, Ninth Australian Conference on Neural Networks (ACNN'98), University of Queensland, Brisbane, Australia, 72-78.
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Last updated: Tuesday, 18.11.2014