Connectionist learning procedures
Artificial Intelligence
Machine Learning - Special issue on inductive transfer
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Gaussian Processes for Classification: Mean-Field Algorithms
Neural Computation
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
A Comparative Study of Methods for Transductive Transfer Learning
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Adaptive mixtures of local experts
Neural Computation
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Learning from Multiple Sources
The Journal of Machine Learning Research
Convex multi-task feature learning
Machine Learning
Semisupervised Multitask Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A model of inductive bias learning
Journal of Artificial Intelligence Research
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Discriminative Learning Under Covariate Shift
The Journal of Machine Learning Research
Bayesian multitask learning with latent hierarchies
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Semi-Supervised Learning
IEEE Transactions on Knowledge and Data Engineering
The Binormal Assumption on Precision-Recall Curves
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Expectation propagation for approximate Bayesian inference
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Domain Adaptation via Transfer Component Analysis
IEEE Transactions on Neural Networks
Bayesian Multitask Classification With Gaussian Process Priors
IEEE Transactions on Neural Networks - Part 1
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We propose a novel model for meta-generalisation, that is, performing prediction on novel tasks based on information from multiple different but related tasks. The model is based on two coupled Gaussian processes with structured covariance function; one model performs predictions by learning a constrained covariance function encapsulating the relations between the various training tasks, while the second model determines the similarity of new tasks to previously seen tasks. We demonstrate empirically on several real and synthetic data sets both the strengths of the approach and its limitations due to the distributional assumptions underpinning it.