Publikationen von B Schölkopf

Smola, A.; Schölkopf, B.: A Tutorial on Support Vector Regression. Statistics and Computing 14 (3), S. 199 - 222 (2004)

Lal, T.; Schröder, M.; Hinterberger, T.; Weston, J.; Bogdan, M.; Birbaumer, N.; Schölkopf, B.: Support Vector Channel Selection in BCI. IEEE Transactions on Biomedical Engineering 51 (6), S. 1003 - 1010 (2004)

von Luxburg, U.; Bousquet, O.; Schölkopf, B.: A Compression Approach to Support Vector Model Selection. The Journal of Machine Learning Research 5, S. 293 - 323 (2004)

Chalimourda, A.; Schölkopf, B.; Smola, A.: Experimentally optimal ν in support vector regression for different noise models and parameter settings. Neural networks 17 (1), S. 127 - 141 (2004)

Schölkopf, B.: Statistische Lerntheorie und Empirische Inferenz. Jahrbuch der Max-Planck-Gesellschaft 2004, S. 377 - 382 (2004)

Schölkopf, B.: Statistical Learning Theory, Capacity and Complexity. Complexity 8 (4), S. 87 - 94 (2003)

Weston, J.; Schölkopf, B.; Eskin, E.; leslie, C.; Noble, W.: Dealing with large Diagonals in Kernel Matrices. Annals of the Institute of Statistical Mathematics 55 (2), S. 391 - 408 (2003)

Mika, S.; Rätsch, G.; Weston, J.; Schölkopf, B.; Smola, A.; Müller, K.-R.: Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (5), S. 623 - 628 (2003)

Weston, J.; Perez-Cruz, F.; Bousquet, O.; Chapelle, O.; Elisseeff, A.; Schölkopf, B.: Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinformatics 19 (6), S. 764 - 771 (2003)

Weston, J.; Elisseeff, A.; Schölkopf, B.; Tipping, M.: Use of the Zero-Norm with Linear Models and Kernel Methods. The Journal of Machine Learning Research 3, S. 1439 - 1461 (2003)

Cristianini, N.; Schölkopf, B.: Support Vector Machines and Kernel Methods: The New Generation of Learning Machines. AI Magazine 23 (3), S. 31 - 41 (2002)

Rätsch, G.; Mika, S.; Schölkopf, B.; Müller, K.-R.: Constructing Boosting algorithms from SVMs: an application to one-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (9), S. 1184 - 1199 (2002)

DeCoste, D.; Schölkopf, B.: Training invariant support vector machines. Machine Learning 46 (1-3), S. 161 - 190 (2002)

Williamson, R.; Smola, A.; Schölkopf, B.: Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. IEEE Transactions on Information Theory 47 (6), S. 2516 - 2532 (2001)

Smola, A.; Mika, S.; Schölkopf, B.; Williamson, R.: Regularized principal manifolds. The Journal of Machine Learning Research 1, S. 179 - 209 (2001)

Müller, K.-R.; Mika, S.; Rätsch, G.; Tsuda, K.; Schölkopf, B.: An Introduction to Kernel-Based Learning Algorithms. IEEE Transactions on Neural Networks 12 (2), S. 181 - 201 (2001)

Schölkopf, B.; Platt, J.; Shawe-Taylor , J.; Smola, A.; Williamson, R.: Estimating the support of a high-dimensional distribution. Neural computation 13 (7), S. 1443 - 1471 (2001)

Schölkopf, B.; Smola, A.; Williamson, R.; Bartlett, P.: New Support Vector Algorithms. Neural computation 12 (5), S. 1207 - 1245 (2000)

Schölkopf, B.; Mika, S.; Burges, C.; Knirsch, P.; Müller, K.-R.; Rätsch, G.; Smola, A.: Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks 10 (5), S. 1000 - 1017 (1999)

Schölkopf, B.; Müller, K.-R.; Smola, A.: Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. Informatik - Forschung und Entwicklung 14 (3), S. 154 - 163 (1999)