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Alex Smola

 

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Alex Smola

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Conference papers (71):

Ong CS, Smola AJ and Williamson RC (October-2003) Hyperkernels In: Advances in Neural Information Processing Systems 15, , Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002), MIT Press, Cambridge, MA, USA, 495-502.
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Ong CS and Smola AJ (August-2003) Machine Learning with Hyperkernels, Twentieth International Conference on Machine Learning (ICML 2003), AAAI Press, Menlo Park, CA, USA, 568-575.
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Gretton A, Herbrich R and Smola A (April-2003) The Kernel Mutual Information, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03), IEEE, Piscataway, NJ, USA, 880-883.
Schölkopf B and Smola AJ (2003) A Short Introduction to Learning with Kernels In: Advanced Lectures on Machine Learning, , Machine Learning Summer School 2002, Springer, Berlin, Germany, 41-64, Series: Lecture Notes in Computer Science ; 2600.
Smola AJ and Schölkopf B (2003) Bayesian Kernel Methods In: Advanced Lectures on Machine Learning, , Machine Learning Summer School 2002, Springer, Berlin, Germany, 65-117, Series: Lecture Notes in Computer Science ; 2600.
Smola AJ, Mangasarian O and Schölkopf B (2002) Sparse kernel feature analysis In: Classification, Automation, and New Media, , 24th Annual Conference of the Gesellschaft für Klassifikation 2000, Springer, Berlin, Germany, 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, 416-426, Series: Lecture Notes in Computer Science ; 2111.
Mika S, Schölkopf B and Smola AJ (January-2001) An Improved Training Algorithm for Kernel Fisher discriminants, 8th International Conference on Artificial Intelligence and Statistics (AISTATS 2001), Morgan Kaufman, San Francisco, CA, USA, 98-104.
Chalimourda A, Schölkopf B and Smola AJ (July-2000) Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000), IEEE, Piscataway, NJ, USA, 199-204.
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.
Mika S, Schölkopf B, Smola AJ, Müller K-R, Scholz M and Rätsch G (June-1999) Kernel PCA and De-noising in feature spaces In: Advances in Neural Information Processing Systems 11, , Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998), MIT Press, Cambridge, MA, USA, 536-542.
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Smola AJ, Friess T and Schölkopf B (June-1999) Semiparametric support vector and linear programming machines In: Advances in Neural Information Processing Systems 11, , Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998), MIT Press, Cambridge, MA, USA, 585-591.
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Schölkopf B, Bartlett PL, Smola AJ and Williamson R (June-1999) Shrinking the tube: a new support vector regression algorithm In: Advances in Neural Information Processing Systems 11, , Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998), MIT Press, Cambridge, MA, USA, 330-336.
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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.
Schölkopf B, Bartlett P, Smola AJ and Williamson R (September-1998) Support vector regression with automatic accuracy control. In: ICANN 98, , 8th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, ICANN'98, 111-116, 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, 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, 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.
Schölkopf B, Knirsch P, Smola AJ and Burges C (1998) Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces In: Mustererkennung 1998, , 20th DAGM-Symposium, Springer, Berlin, Germany, 125-132, Series: Informatik aktuell.
Schölkopf B, Smola AJ and Müller K-R (October-1997) Kernel principal component analysis In: Artificial Neural Networks - ICANN '97, , 7th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 583-588, Series: Lecture Notes in Computer Science ; 1327.
Müller K-R, Smola AJ, Rätsch G, Schölkopf B, Kohlmorgen J and Vapnik V (October-1997) Predicting time series with support vector machines In: Artificial Neural Networks - ICANN '97, , 7th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 999-1004, Series: Lecture Notes in Computer Science ; 1327.
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Contributions to books (4):

Gretton A, Smola AJ, Huang J, Schmittfull M, Borgwardt KM and Schölkopf B: Covariate Shift by Kernel Mean Matching, 131-160. In: Dataset Shift in Machine Learning, (Ed) J. Quiñonero Candela, MIT Press, Cambridge, MA, USA, (February-2009).
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Altun Y and Smola AJ: Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces, 283-300. In: Predicting Structured Data, (Ed) G. H. BakIr, MIT Press, Cambridge, MA, USA, (September-2007).
Schölkopf B and Smola AJ: Support Vector Machines and Kernel Algorithms, 5328-5335. In: Encyclopedia of Biostatistics, (Ed) P. Armitage, Wiley, Chichester, UK, (2005).
Schölkopf B and Smola AJ: Support Vector Machines, 1119-1125. In: Handbook of Brain Theory and Neural Networks (sec. ed.), (Ed) A. Michael, MIT Press, Cambridge, MA, USA, (November-2002).

Technical reports (14):

Gretton A, Borgwardt K, Rasch M, Schölkopf B and Smola A: A Kernel Method for the Two-sample Problem, 157, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, (April-2008).
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Last updated: Monday, 22.05.2017