Suchergebnisse

Konferenzbeitrag (60)

81.
Konferenzbeitrag
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, S. 105 - 110 (Hg. Niklasson, L.; Bodén, M.; Ziemke, T.). 8th International Conference on Artificial Neural Networks (ICANN 1998), Skövde, Sweden, 02. September 1998 - 04. September 1998. Springer, London, UK (1998)
82.
Konferenzbeitrag
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, S. 99 - 104 (Hg. Niklasson, L.; Bodén, M.; Ziemke, T.). 8th International Conference on Artificial Neural Networks (ICANN 1998), Skövde, Sweden, 02. September 1998 - 04. September 1998. Springer, London, UK (1998)
83.
Konferenzbeitrag
Schölkopf, B.; Simard, P.; Smola, A.; Vapnik, V.: Prior knowledge in support vector kernels. In: Advances in Neural Information Processing Systems 10, S. 640 - 646 (Hg. Jordan, M.; Kearns, M.; Solla, S.). Eleventh Annual Conference on Neural Information Processing (NIPS 1997), Denver, CO, USA, 01. Dezember 1997 - 06. Dezember 1997. MIT Press, Cambridge, MA, USA (1998)
84.
Konferenzbeitrag
Smola, A.; Schölkopf, B.: From regularization operators to support vector kernels. In: Advances in Neural Information Processing Systems 10, S. 343 - 349 (Hg. Jordan, M.; Kearns, M.; Solla, S.). Eleventh Annual Conference on Neural Information Processing (NIPS 1997), Denver, CO, USA, 01. Dezember 1997 - 06. Dezember 1997. MIT Press, Cambridge, MA, USA (1998)
85.
Konferenzbeitrag
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), S. 72 - 78 (Hg. Downs, T.; Frean, M.; Gallagher, M.). Ninth Australian Conference on Neural Networks (ACNN 1998), Brisbane, Australia, 11. Februar 1998 - 13. Februar 1998. St. Lucia (1998)
86.
Konferenzbeitrag
Smola, A.; Schölkopf, B.; Müller, K.-R.: General cost functions for support vector regression. In: Ninth Australian Conference on Neural Networks (ACNN 1998), S. 79 - 83 (Hg. Downs, T.; Frean, M.; Gallagher, M.). Ninth Australian Conference on Neural Networks (ACNN 1998), Brisbane, Australia, 11. Februar 1998 - 13. Februar 1998. St. Lucia (1998)
87.
Konferenzbeitrag
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, S. 999 - 1004 (Hg. Gerstner, W.; Germond, A.; Hasler, M.; Nicoud, J.-D.). 7th International Conference on Artificial Neural Networks (ICANN 1997), Lausanne, Switzerland, 08. Oktober 1997 - 10. Oktober 1997. Springer, Berlin, Germany (1997)
88.
Konferenzbeitrag
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, S. 583 - 588 (Hg. Gerstner, W.; Germond, A.; Hasler, M.; Nicoud, J.-D.). 7th International Conference on Artificial Neural Networks (ICANN 1997), Lausanne, Switzerland, 08. Oktober 1997 - 10. Oktober 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)

Vortrag (2)

90.
Vortrag
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.
Vortrag
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)

Bericht (13)

95.
Bericht
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 S.
96.
Bericht
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 S.
97.
Bericht
Gretton, A.; Herbrich, R.; Smola, A.: The Kernel Mutual Information. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2003), 91 S.
98.
Bericht
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 S.
99.
Bericht
Schölkopf, B.; Platt, J.; Smola, A.: Kernel method for percentile feature extraction. Microsoft Research, Microsoft Corporation, Redmond, WA, USA (2000)
100.
Bericht
Smola, A.; Mangasarian, O.; Schölkopf, B.: Sparse Kernel Feature Analysis. University of Wisconsin, Data Mining Institute, Madison, WI, USA (1999), 21 S.
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