Machine Learning - Special issue on inductive transfer
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Logistic regression with an auxiliary data source
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
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Kernel-Based Inductive Transfer
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees
DS '08 Proceedings of the 11th International Conference on Discovery Science
Learning to Recognize Activities from the Wrong View Point
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Relevant subtask learning by constrained mixture models
Intelligent Data Analysis
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We introduce a problem called relevant subtask learning, a variant of multi-task learning. The goal is to build a classifier for a task-of-interest having too little data. We also have data for other tasks but only some are relevant, meaning they contain samples classified in the same way as in the task-of-interest. The problem is how to utilize this "background data" to improve the classifier in the task-of-interest. We show how to solve the problem for logistic regression classifiers, and show that the solution works better than a comparable multi-task learning model. The key is to assume that data of all tasks are mixtures of relevant and irrelevant samples, and model the irrelevant part with a sufficiently flexible model such that it does not distort the model of relevant data.