Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18321
Title: Generalized hidden-mapping ridge regression, knowledge-leveraged inductive transfer learning for neural networks, fuzzy systems and kernel methods
Authors: Deng, Z
Choi, KS 
Jiang, Y
Wang, S
Keywords: Classification
Fuzzy systems
Generalized hidden-mapping ridge regression (GHRR)
Inductive transfer learning
Kernel methods
Knowledge-leverage
Neural networks
Regression
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2014, v. 44, no. 12, 2311014, p. 2585-2599 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: Inductive transfer learning has attracted increasing attention for the training of effective model in the target domain by leveraging the information in the source domain. However, most transfer learning methods are developed for a specific model, such as the commonly used support vector machine, which makes the methods applicable only to the adopted models. In this regard, the generalized hidden-mapping ridge regression (GHRR) method is introduced in order to train various types of classical intelligence models, including neural networks, fuzzy logical systems and kernel methods. Furthermore, the knowledge-leverage based transfer learning mechanism is integrated with GHRR to realize the inductive transfer learning method called transfer GHRR (TGHRR). Since the information from the induced knowledge is much clearer and more concise than that from the data in the source domain, it is more convenient to control and balance the similarity and difference of data distributions between the source and target domains. The proposed GHRR and TGHRR algorithms have been evaluated experimentally by performing regression and classification on synthetic and real world datasets. The results demonstrate that the performance of TGHRR is competitive with or even superior to existing state-of-the-art inductive transfer learning algorithms.
URI: http://hdl.handle.net/10397/18321
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2014.2311014
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