Publikationen von A Smola

Gretton, A.; Borgwardt, K.; Rasch, M.; Schölkopf, B.; Smola, A.: A Kernel Two-Sample Test. Journal of Machine Learning Research 13, S. 723 - 773 (2012)

Song, L.; Smola, A.; Gretton, A.; Bedo, J.; Borgwardt, K.: Feature Selection via Dependence Maximization. Journal of Machine Learning Research 13, S. 1393 - 1434 (2012)

Thoma, M.; Cheng, H.; Gretton, A.; Han, J.; Kriegel, H.-P.; Smola, A.; Song, L.; Yu, P.; Yan, X.; Borgwardt, K.: Discriminative frequent subgraph mining with optimality guarantees. Statistical Analysis and Data Mining 3 (5), S. 302 - 318 (2010)

Vishwanathan, S.; Borgwardt, K.; Guttman, O.; Smola, A.: Kernel extrapolation. Neurocomputing 69 (7-9), S. 721 - 729 (2006)

Gretton, A.; Herbrich, R.; Smola, A.; Bousquet, O.; Schölkopf, B.: Kernel Methods for Measuring Independence. The Journal of Machine Learning Research 6, S. 2075 - 2129 (2005)

Ong, C.; Smola, A.; Williamson, R.: Learning the Kernel with Hyperkernels. The Journal of Machine Learning Research 6, S. 1043 - 1071 (2005)

Borgwardt, K.; Ong, C.; Schönauer, S.; Vishwanathan , S.; Smola, A.; Kriegel, H.-P.: Protein function prediction via graph kernels. Bioinformatics 21 (Supplement 1), S. i47 - i56 (2005)

Chalimourda, A.; Schölkopf, B.; Smola, A.: Experimentally optimal ν in support vector regression for different noise models and parameter settings. Neural networks 18 (2), S. 205 - 205 (2005)

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

Graf, A.; Smola, A.; Borer, S.: Classification in a Normalized Feature Space using Support Vector Machines. IEEE Transactions on Neural Networks 14 (3), S. 597 - 605 (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)

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)

Smola, A.; Schölkopf, B.: On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion. Algorithmica 22 (1-2), S. 211 - 231 (1998)

Schölkopf, B.; Smola, A.; Müller, K.-R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural computation 10 (5), S. 1299 - 1319 (1998)

Smola, A.; Schölkopf, B.; Müller, K.-R.: The connection between regularization operators and support vector kernels. Neural networks 11 (4), S. 637 - 649 (1998)

Schölkopf, B.; Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA (2002), 626 S.