Machine Learning - Special issue on inductive transfer
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Hierarchic Bayesian models for kernel learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning a kernel function for classification with small training samples
ICML '06 Proceedings of the 23rd international conference on Machine learning
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Learning from Relevant Tasks Only
ECML '07 Proceedings of the 18th European conference on Machine Learning
A model of inductive bias learning
Journal of Artificial Intelligence Research
Relevant subtask learning by constrained mixture models
Intelligent Data Analysis
Transfer learning with adaptive regularizers
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Compact coding for hyperplane classifiers in heterogeneous environment
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Classifier ensemble recommendation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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Methods for inductive transfer take advantage of knowledge from previous learning tasks to solve a newly given task. In the context of supervised learning, the task is to find a suitable bias for a new dataset, given a set of known datasets. In this paper, we take a kernel-based approach to inductive transfer, that is, we aim at finding a suitable kernel for the new data. In our setup, the kernel is taken from the linear span of a set of predefined kernels. To find such a kernel, we apply convex optimization on two levels. On the base level, we propose an iterative procedure to generate kernels that generalize well on the known datasets. On the meta level, we combine those kernels in a minimization criterion to predict a suitable kernel for the new data. The criterion is based on a meta kernel capturing the similarity of two datasets. In experiments on small molecule and text data, kernel-based inductive transfer showed a statistically significant improvement over the best individual kernel in almost all cases.