Journal ArticleDOI
A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification
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TLDR
It is shown how to introduce the concepts of fuzzy learning into DL to overcome the shortcomings of fixed representation and the fuzzy dDL paradigm greatly outperforms other nonfuzzy and shallow learning approaches on these tasks.Abstract:
Deep learning (DL) is an emerging and powerful paradigm that allows large-scale task-driven feature learning from big data. However, typical DL is a fully deterministic model that sheds no light on data uncertainty reductions. In this paper, we show how to introduce the concepts of fuzzy learning into DL to overcome the shortcomings of fixed representation. The bulk of the proposed fuzzy system is a hierarchical deep neural network that derives information from both fuzzy and neural representations. Then, the knowledge learnt from these two respective views are fused altogether forming the final data representation to be classified. The effectiveness of the model is verified on three practical tasks of image categorization, high-frequency financial data prediction and brain MRI segmentation that all contain high level of uncertainties in the raw data. The fuzzy dDL paradigm greatly outperforms other nonfuzzy and shallow learning approaches on these tasks.read more
Citations
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Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification
Shuangg Feng,C. L. Philip Chen +1 more
TL;DR: A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by merging the Takagi–Sugeno (TS) fuzzy system into BLS, and the results indicate that fuzzy BLS outperforms other models involved.
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Deep Fuzzy Hashing Network for Efficient Image Retrieval
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Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to?
TL;DR: It will be pointed out why evolutionary fuzzy systems are important from an explainable point of view, when they began, what they are used for, and where the attention of researchers should be directed to in the near future in this area.
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Xavier Glorot,Yoshua Bengio +1 more
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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
Book
Learning Deep Architectures for AI
TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.