Abstract
This research proposes a novel transfer learning algorithm, Noise- Label Transfer Learning (NLTL), aiming at exploiting noisy (in terms of labels and features) training data to improve the learning quality. We exploit the information from both accurate and noisy data by transferring the features into common domain and adjust the weights of instances for learning. We experiment on three University of California Irvine (UCI) datasets and one real-world dataset (Plurk) to evaluate the effectiveness of the model.
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© 2014 Springer International Publishing Switzerland
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Lin, WS., Kuo, TT., Huang, YY., Lu, WC., Lin, SD. (2014). A Transfer-Learning Approach to Exploit Noisy Information for Classification and Its Application on Sentiment Detection. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_25
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DOI: https://doi.org/10.1007/978-3-319-13987-6_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13986-9
Online ISBN: 978-3-319-13987-6
eBook Packages: Computer ScienceComputer Science (R0)