Learning from Relevant Tasks Only
Samuel Kaski,Jaakko Peltonen +1 more
- 17 Sep 2007
- pp 608-615
TL;DR: This work introduces a problem called relevant subtask learning, a variant of multi-task learning, to build a classifier for a task-of-interest having too little data, and shows how to solve the problem for logistic regression classifiers and that the solution works better than a comparable multi- task learning model.
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Abstract: 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.
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References
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Rich Caruana
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TL;DR: Multi-task Learning (MTL) as mentioned in this paper is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias.
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Avrim Blum,Tom M. Mitchell +1 more
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TL;DR: A PAC-style analysis is provided for a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views, to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples.
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Prem Melville,Raymod J. Mooney,Ramadass Nagarajan +2 more
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TL;DR: This paper presents an elegant and effective framework for combining content and collaboration, which uses a content-based predictor to enhance existing user data, and then provides personalized suggestions through collaborative filtering.
Learning Multiple Tasks with Kernel Methods
TL;DR: The experiments show that learning multiple related tasks simultaneously using the proposed approach can significantly outperform standard single-task learning particularly when there are many related tasks but few data per task.
Task clustering and gating for bayesian multitask learning
Bart Bakker,Tom Heskes +1 more
TL;DR: A Bayesian approach is adopted in which some of the model parameters are shared and others more loosely connected through a joint prior distribution that can be learned from the data to combine the best parts of both the statistical multilevel approach and the neural network machinery.