Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Inference for the Generalization Error
Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Column-generation boosting methods for mixture of kernels
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Weighted decomposition kernels
ICML '05 Proceedings of the 22nd international conference on Machine learning
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Model Selection: Beyond the Bayesian/Frequentist Divide
The Journal of Machine Learning Research
A unifying view of multiple kernel learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
Hi-index | 0.00 |
The choice of a suitable graph kernel is intrinsically hard and often cannot be made in an informed manner for a given dataset. Methods for multiple kernel learning offer a possible remedy, as they combine and weight kernels on the basis of a labeled training set of molecules to define a new kernel. Whereas most methods for multiple kernel learning focus on learning convex linear combinations of kernels, we propose to combine kernels in products, which theoretically enables higher expressiveness. In experiments on ten publicly available chemical QSAR datasets we show that product kernel learning is on no dataset significantly worse than any of the competing kernel methods and on average the best method available. A qualitative analysis of the resulting product kernels shows how the results vary from dataset to dataset.