期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 23, 期 6, 页码 2163-2173出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2015.2406889
关键词
Deep learning; fuzzy deep networks; fuzzy restricted Boltzmann machine; image classification; image inpainting; restricted Boltzmann machine (RBM)
资金
- Macau Science and Technology Development Fund [008/2010/A1]
- UM Multiyear Research Grants
- National Natural Science Foundation of China [61203106]
In recent years, deep learning caves out a research wave in machine learning. With outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of existing deep networks are based on or related to it. For regular RBM, the relationships between visible units and hidden units are restricted to be constants. This restriction will certainly downgrade the representation capability of the RBM. To avoid this flaw and enhance deep learning capability, the fuzzy restricted Boltzmann machine (FRBM) and its learning algorithm are proposed in this paper, in which the parameters governing the model are replaced by fuzzy numbers. This way, the original RBM becomes a special case in the FRBM, when there is no fuzziness in the FRBM model. In the process of learning FRBM, the fuzzy free energy function is defuzzified before the probability is defined. The experimental results based on bar-and-stripe benchmark inpainting and MNIST handwritten digits classification problems show that the representation capability of FRBM model is significantly better than the traditional RBM. Additionally, the FRBM also reveals better robustness property compared with RBM when the training data are contaminated by noises.
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