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- W1560483044 abstract "Multiple classifier systems have been originally proposed for supervised classification tasks, and few works have dealt with semi-supervised multiple classifiers. However, there are important pattern recognition applications, such as multi-sensor remote sensing and multi-modal biometrics, which demand semi-supervised multiple classifier systems able to exploit both labelled and unlabelled data. In this paper, the use, in multiple classifier systems, of two well known semi-supervised learning methods, namely, co-training and self-training, is investigated by experiments. Reported results on benchmarking data sets show that co-training and self-training allow exploiting unlabelled data in different types of multiple classifiers systems." @default.
- W1560483044 created "2016-06-24" @default.
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- W1560483044 date "2006-01-01" @default.
- W1560483044 modified "2023-09-25" @default.
- W1560483044 title "Using Co-training and Self-training in Semi-supervised Multiple Classifier Systems" @default.
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- W1560483044 doi "https://doi.org/10.1007/11815921_57" @default.
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