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- W2912205977 abstract "In real-world machine learning applications, we are often faced with a situation where only a small number of training samples is available due to high sampling costs. For instance, prediction of mental states such as drowsiness from physiological information is a typical example. To cope with this problem, classifier training methods only from positive and unlabeled data and multi-task learning methods for improving the classification performance by solving multiple related tasks simultaneously have been actively investigated recently. In this paper, we combine these methods and propose a multitask learning method that can handle positive-unlabeled tasks and positive-negative tasks in a unified manner. Through experiments on drivers' drowsiness prediction, we demonstrate the effectiveness of the proposed method." @default.
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- W2912205977 date "2017-11-02" @default.
- W2912205977 modified "2023-09-28" @default.
- W2912205977 title "Multi-Task Learning with Positive and Unlabeled Data and Its Application to Mental State Prediction" @default.
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