Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Transferring previously learned back-propagation neural networks to new learning tasks
Transferring previously learned back-propagation neural networks to new learning tasks
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personalized handwriting recognition via biased regularization
ICML '06 Proceedings of the 23rd international conference on Machine learning
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
The Journal of Machine Learning Research
Relaxed Transfer of Different Classes via Spectral Partition
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Adapted transfer of distance measures for quantitative structure-activity relationships
DS'10 Proceedings of the 13th international conference on Discovery science
| Hi-index | 0.00 |
In this paper, we propose a novel graph-based method for knowledge transfer. We model the transfer relationships between source tasks by embedding the set of learned source models in a graph using transferability as the metric. Transfer to a new problem proceeds by mapping the problem into the graph, then learning a function on this graph that automatically determines the parameters to transfer to the new learning task. This method is analogous to inductive transfer along a manifold that captures the transfer relationships between the tasks. We demonstrate improved transfer performance using this method against existing approaches in several real-world domains.