Machine Learning - Special issue on inductive transfer
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Bounds for Linear Multi-Task Learning
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Multi-task reinforcement learning: a hierarchical Bayesian approach
Proceedings of the 24th international conference on Machine learning
Convex multi-task feature learning
Machine Learning
A model of inductive bias learning
Journal of Artificial Intelligence Research
Towards a Theoretical Framework for Learning Multi-modal Patterns for Embodied Agents
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Vector field learning via spectral filtering
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Relevant subtask learning by constrained mixture models
Intelligent Data Analysis
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
Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning
SIAM Journal on Optimization
Kernels for Vector-Valued Functions: A Review
Foundations and Trends® in Machine Learning
An overview of transfer learning and computational cyberpsychology
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Learning with infinitely many features
Machine Learning
Multi-task averaging via task clustering
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
| Hi-index | 0.00 |
We consider the problem of learning in an environment of classification tasks. Tasks sampled from the environment are used to improve classification performance on future tasks. We consider situations in which the tasks can be divided into groups. Tasks within each group are related by sharing a low dimensional representation, which differs across the groups. We present an algorithm which divides the sampled tasks into groups and computes a common representation for each group. We report experiments on a synthetic and two image data sets, which show the advantage of the approach over single-task learning and a previous transfer learning method.