Summary of Efficient Learning Of Fuzzy Logic Systems For Large-scale Data Using Deep Learning, by Ata Koklu et al.
Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning
by Ata Koklu, Yusuf Guven, Tufan Kumbasar
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel learning method that combines Type-1 and Interval Type-2 (IT2) Fuzzy Logic Systems (FLS) with Deep Learning (DL), addressing the complexity issues in training large-scale FLSs. The proposed method leverages DL frameworks to implement computationally efficient implementations of FLSs, minimizing training time while utilizing mini-batched optimizers and automatic differentiation. The approach is evaluated on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make fuzzy logic systems learn from big data faster and more efficiently. It combines two things: fuzzy logic and deep learning. This helps solve problems like the “curse of dimensionality” that can happen when dealing with large amounts of data. The new method is tested on some standard datasets. |
Keywords
* Artificial intelligence * Deep learning
