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 |
Issue Date: | Dec-2014 | Source: | IEEE transactions on cybernetics, Dec, 2014, v. 44, no. 12, p. 2585-2599 | 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. | Keywords: | Classification Fuzzy systems Generalized hidden-mapping ridge regression (GHRR) Inductive transfer learning Kernel methods Knowledge-leverage Neural networks Regression |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on cybernetics | ISSN: | 2168-2267 | EISSN: | 2168-2275 | DOI: | 10.1109/TCYB.2014.2311014 | Rights: | © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication Z. Deng, K. Choi, Y. Jiang and S. Wang, "Generalized Hidden-Mapping Ridge Regression, Knowledge-Leveraged Inductive Transfer Learning for Neural Networks, Fuzzy Systems and Kernel Methods," in IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2585-2599, Dec. 2014 is available at https://dx.doi.org/10.1109/TCYB.2014.2311014. |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| a0597-n13_452.pdf | Pre-Published version | 1.48 MB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
58
Last Week
0
0
Last month
Citations as of May 28, 2023
Downloads
20
Citations as of May 28, 2023
SCOPUSTM
Citations
112
Last Week
0
0
Last month
2
2
Citations as of May 25, 2023
WEB OF SCIENCETM
Citations
108
Last Week
0
0
Last month
2
2
Citations as of May 25, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.