[1]XU Min,SHI Yingzhong,GE Hongwei,et al.A RBF-NN algorithm with transfer learning ability and its application[J].CAAI Transactions on Intelligent Systems,2018,13(6):959-966.[doi:10.11992/tis.201705021]
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A RBF-NN algorithm with transfer learning ability and its application
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 6
Page number:
959-966
Column:
学术论文—机器学习
Public date:
2018-10-25
- Title:
- A RBF-NN algorithm with transfer learning ability and its application
- Keywords:
- radial basis function neural network; transfer learning; radial basis function vector; ε-insensitive loss function; missing information
- CLC:
- TP391
- DOI:
- 10.11992/tis.201705021
- Abstract:
- The classical radial basis function neural network (RBF-NN) is widely used as it can approximate any function. However, one of its main defects is that overfitting is likely to occur when there are too few labeled samples to reflect the overall distribution of datasets; this leads to a serious decline in its generalization ability. To solve the above problem, an artificial RBF-NN learning algorithm with transfer learning ability is discussed. The algorithm introduces the ε-insensitive loss function and the structural risk term and also learns the center vector and kernel width of the radial basis function as well as the parameters of the source domain model. The algorithm fully learns the knowledge in the historical source domain to compensate for its decline in generalization ability caused by the lack of labeled samples in the current field. To verify the algorithm, it is applied to an artificial dataset and real fermentation dataset. Compared with the traditional RBF-NN algorithm, the proposed algorithm has a better adaptability as regards less labeled samples and missing data.
- References:
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Last Update:
2018-12-25