Machine Learning - Special issue on inductive transfer
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
A bartering approach to improve multiagent learning
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Multi-task feature and kernel selection for SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning from Multiple Sources
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Automatic Feature Extraction for Classifying Audio Data
Machine Learning
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Information preserving multi-objective feature selection for unsupervised learning
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Robust multi-task learning with t-processes
Proceedings of the 24th international conference on Machine learning
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Finding the right data representation is essential for virtually every machine learning task. We discuss an extension of this representation problem. In the collaborative representation problem, the aim is to find for each learning agent in a multi-agent system an optimal data representation, such that the overall performance of the system is optimized, while not assuming that all agents learn the same underlying concept. Also, we analyze the problem of keeping the common terminology in which agents express their hypothesis as compact and comprehensible as possible by forcing them to use the same features, where this is possible. We analyze the complexity of this problem and show under which conditions an optimal solution can be found. We then propose a simple heuristic algorithm and show that this algorithm can efficiently be implemented in a multi-agent system. The approach is exemplified on the problem of collaborative media organization and evaluated on a several synthetic and real world datasets.