Search results

Conference Paper (60)

81.
Conference Paper
Smola, A.; Murata, N.; Schölkopf, B.; Müller, K.-R.: Asymptotically Optimal Choice of ε-Loss for Support Vector Machines. In: ICANN 98: 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998, pp. 105 - 110 (Eds. Niklasson, L.; Bodén, M.; Ziemke, T.). 8th International Conference on Artificial Neural Networks (ICANN 1998), Skövde, Sweden, September 02, 1998 - September 04, 1998. Springer, London, UK (1998)
82.
Conference Paper
Smola, A.; Schölkopf, B.; Müller, K.-R.: Convex Cost Functions for Support Vector Regression. In: ICANN 98: 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998, pp. 99 - 104 (Eds. Niklasson, L.; Bodén, M.; Ziemke, T.). 8th International Conference on Artificial Neural Networks (ICANN 1998), Skövde, Sweden, September 02, 1998 - September 04, 1998. Springer, London, UK (1998)
83.
Conference Paper
Schölkopf, B.; Simard, P.; Smola, A.; Vapnik, V.: Prior knowledge in support vector kernels. In: Advances in Neural Information Processing Systems 10, pp. 640 - 646 (Eds. Jordan, M.; Kearns, M.; Solla, S.). Eleventh Annual Conference on Neural Information Processing (NIPS 1997), Denver, CO, USA, December 01, 1997 - December 06, 1997. MIT Press, Cambridge, MA, USA (1998)
84.
Conference Paper
Smola, A.; Schölkopf, B.: From regularization operators to support vector kernels. In: Advances in Neural Information Processing Systems 10, pp. 343 - 349 (Eds. Jordan, M.; Kearns, M.; Solla, S.). Eleventh Annual Conference on Neural Information Processing (NIPS 1997), Denver, CO, USA, December 01, 1997 - December 06, 1997. MIT Press, Cambridge, MA, USA (1998)
85.
Conference Paper
Schölkopf, B.; Smola, A.; Müller, K.-R.; Burges, C.; Vapnik, V.: Support Vector methods in learning and feature extraction. In: Ninth Australian Conference on Neural Networks (ACNN 1998), pp. 72 - 78 (Eds. Downs, T.; Frean, M.; Gallagher, M.). Ninth Australian Conference on Neural Networks (ACNN 1998), Brisbane, Australia, February 11, 1998 - February 13, 1998. St. Lucia (1998)
86.
Conference Paper
Smola, A.; Schölkopf, B.; Müller, K.-R.: General cost functions for support vector regression. In: Ninth Australian Conference on Neural Networks (ACNN 1998), pp. 79 - 83 (Eds. Downs, T.; Frean, M.; Gallagher, M.). Ninth Australian Conference on Neural Networks (ACNN 1998), Brisbane, Australia, February 11, 1998 - February 13, 1998. St. Lucia (1998)
87.
Conference Paper
Müller, K.-R.; Smola, A.; Rätsch, G.; Schölkopf, B.; Kohlmorgen, J.; Vapnik, V.: Predicting time series with support vector machines. In: Artificial Neural Networks — ICANN'97: 7th International Conference Lausanne, Switzerland, October 8–10, 1997, pp. 999 - 1004 (Eds. Gerstner, W.; Germond, A.; Hasler, M.; Nicoud, J.-D.). 7th International Conference on Artificial Neural Networks (ICANN 1997), Lausanne, Switzerland, October 08, 1997 - October 10, 1997. Springer, Berlin, Germany (1997)
88.
Conference Paper
Schölkopf, B.; Smola, A.; Müller, K.-R.: Kernel principal component analysis. In: Artificial Neural Networks - ICANN'97: 7th International Conference Lausanne, Switzerland, October 8–10, 1997, pp. 583 - 588 (Eds. Gerstner, W.; Germond, A.; Hasler, M.; Nicoud, J.-D.). 7th International Conference on Artificial Neural Networks (ICANN 1997), Lausanne, Switzerland, October 08, 1997 - October 10, 1997. Springer, Berlin, Germany (1997)

Meeting Abstract (1)

89.
Meeting Abstract
Vishwanathan, S.; Guttman, O.; Borgwardt, K.; Smola, A.: Kernel Extrapolations for Enzyme Classification. In NIPS 2004 Workshop on New Problems and Methods in Computational Biology (MLCB 2004). NIPS 2004 Workshop on New Problems and Methods in Computational Biology (MLCB 2004), Vancouver, Canada. (2004)

Talk (2)

90.
Talk
Smola, A.; Gretton, A.; Fukumizu, K.: Painless Embeddings of Distributions: the Function Space View. 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland (2008)
91.
Talk
Gretton, A.; Borgwardt, K.; Fukumizu, K.; Rasch, M.; Schölkopf, B.; Smola, A.; Song , L.; Teo, C.: Hilbert Space Representations of Probability Distributions. 2nd Workshop on Machine Learning and Optimization at the ISM, Tokyo, Japan (2007)

Poster (3)

92.
Poster
Gretton, A.; Smola, A.; Bousquet, O.; Herbrich, R.; Belitski, A.; Augath, M.; Murayama, Y.; Pauls, J.; Schölkopf, B.; Logothetis, N.: Kernel-based dependence detection in the Macaque visual cortex. Computational and Systems Neuroscience Meeting (COSYNE 2005), Salt Lake City, UT, USA (2005)
93.
Poster
Gretton, A.; Smola, A.; Bousquet, O.; Herbrich, R.; Belitski, A.; Augath, M.; Murayama, Y.; Pauls, J.; Schölkopf, B.; Logothetis, N.: Kernel-based measures of dependence in the Macaque visual cortex. Computational and Systems Neuroscience Meeting (COSYNE 2005), Salt Lake City, UT, USA (2005)
94.
Poster
Schölkopf, B.; Williamson, R.; Smola, A.; Shawe-Taylor, J.: Single-class Support Vector Machines. Dagstuhl-Seminar 99121: Unsupervised Learning, Dagstuhl, Germany (1999)

Report (13)

95.
Report
Gretton, A.; Borgwardt, K.; Rasch, M.; Schölkopf, B.; Smola, A.: A Kernel Method for the Two-sample Problem (Technical Report of the Max Planck Institute for Biological Cybernetics, 157). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 44 pp.
96.
Report
Gretton, A.; Smola, A.; Bousquet, O.; Herbrich, R.; Schölkopf, B.; Logothetis, N.: Behaviour and Convergence of the Constrained Covariance (Technical Report of the Max Planck Institute for Biological Cybernetics, 130). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2004), 18 pp.
97.
Report
Gretton, A.; Herbrich, R.; Smola, A.: The Kernel Mutual Information. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2003), 91 pp.
98.
Report
Schölkopf, B.; Platt, J.; Shawe-Taylor, J.; Smola, A.; Williamson, R.: Estimating the support of a high-dimensional distribution. Microsoft Research, Microsoft Corporation, Redmond, VA, USA (2000), 30 pp.
99.
Report
Schölkopf, B.; Platt, J.; Smola, A.: Kernel method for percentile feature extraction. Microsoft Research, Microsoft Corporation, Redmond, WA, USA (2000)
100.
Report
Smola, A.; Mangasarian, O.; Schölkopf, B.: Sparse Kernel Feature Analysis. University of Wisconsin, Data Mining Institute, Madison, WI, USA (1999), 21 pp.
Go to Editor View