Publikationen von Karsten M Borgwardt

Song, L.; Bedo, J.; Borgwardt, K. M.; Gretton, A.; Smola, A.: Gene selection via the BAHSIC family of algorithms. Bioinformatics 23 (13), S. i490 - i498 (2007)

Song, L.; Gretton, A.; Borgwardt, K.; Smola, A.: Colored Maximum Variance Unfolding. Advances in Neural Information Processing Systems 20 (NIPS 2007), S. 1385 - 1392 (2007)

Song, L.; Smola, A.; Gretton, A.; Borgwardt, K.: A dependence maximization view of clustering. Proceedingds of the 24th Annual International Conference on Machine Learning (ICML '07), S. 815 - 822 (2007)

Song, L.; Smola, A.; Gretton, A.; Borgwardt, K. M.; Bedo, J.: Supervised feature selection via dependence estimation. Proceedings of the 24th Annual International Conference on Machine Learning (ICML '07), S. 823 - 830 (2007)

Aßfalg, J.; Borgwardt, K. M.; Kriegel, H.-P.: 3DString: a feature string kernel for 3D object classification on voxelized data. Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM 2006), S. 198 - 207 (2006)

Borgwardt, K. M.; Gretton, A.; Rasch, M. J.; Kriegel, H.-P.; Schölkopf, B.; Smola, A. J.: Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics 22 (14), S. e49 - e57 (2006)

Borgwardt, K. M.; Kriegel, H.-p.; Wackersreuther, P.: Pattern Mining in Frequent Dynamic Subgraphs. Proceedings of the Sixth International Conference on Data Mining (ICDM'06), S. 818 - 822 (2006)

Borgwardt, K. M.; Schraudolph, N.; Vishwanathan, S. V. N.: Fast Computation of Graph Kernels. Advances in Neural Information Processing Systems 19 (NIPS 2006), S. 1449 - 1456 (2006)

Borgwardt, K. M.; Vishwanathan, S. V. N.; Kriegel, H.-P.: Class Prediction from Time Series Gene Expression Profiles using Dynamical Systems Kernels. Proceedings of the Pacific Symposium (PSB 2006), S. 547 - 558 (2006)

Gretton, A.; Borgwardt, K. M.; Rasch, M.; Schölkopf, B.; Smola, A.: A Kernel Method for the Two-Sample-Problem. Advances in Neural Information Processing Systems 19 (NIPS 2006), S. 513 - 520 (2006)

Huang, J.; Gretton, A.; Borgwardt, K. M.; Schölkopf, B.; Smola, A.: Correcting Sample Selection Bias by Unlabeled Data. Advances in Neural Information Processing Systems 20 (NIPS 2006), S. 601 - 608 (2006)

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

Borgwardt, K. M.; Guttman, O.; Vishwanathan, S. V. N.; Smola, A. J.: Joint Regularization. Proceedings of the 13th European Symposium on Artificial Neural Networks (ESANN 2005), S. 455 - 460 (2005)

Borgwardt, K. M.; Kriegel, H.: Shortest-Path Kernels on Graphs. Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM'05) (2005)

Borgwardt, K. M.; Ong, C. S.; Schonauer, S.; Vishwanathan, S. V. N.; Smola, A. J.; Kriegel, H. P.: Protein function prediction via graph kernels. Bioinformatics 21 (Suppl 1), S. i47 - i56 (2005)

Bock, C.; Moor, M.; Jutzeler, C. R.; Borgwardt, K.: Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning. In: Artificial Neural Networks, Bd. 2190, S. 33 - 71 (Hg. Cartwright, H.) (2020)

Llinares-​López, F.; Borgwardt, K.: Machine Learning for Biomarker Discovery: Significant Pattern Mining. In: Analyzing Network Data in Biology and Medicine, S. 313 - 368 (Hg. Pržulj, N.). Cambridge University Press (2019)

Gumpinger, A. C.; Roqueiro, D.; Grimm, D. G.; Borgwardt, K. M.: Methods and Tools in Genome-wide Association Studies. In: Computational Cell Biology, Bd. 1819, S. 93 - 136 (Hg. von Stechow, L.; Santos Delgado, A.). Springer New York, New York, NY (2018)

He, X.; Li, L.; Roqueiro, D.; Borgwardt, K.: Multi-view Spectral Clustering on Conflicting Views. In: Machine Learning and Knowledge Discovery in Databases, Bd. 10535, S. 826 - 842 (Hg. Ceci, M.; Hollmén, J.; Todorovski, L.; Vens, C.; Džeroski, S.). Springer International Publishing, Cham (2017)

Sugiyama, M.; Azencott, C.-A.; Grimm, D.; Kawahara, Y.; Borgwardt, K.: Multi-Task Feature Selection on Multiple Networks via Maximum Flows. In: Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), S. 199 - 207 (2014)