A novel adversarial deep TSK fuzzy classifier with its inverse-free fast training
Abstract Deep learning is one of the most popular machine learning methods, and has been used in many applications. However, conventional deep learning is a fully deterministic model that has difficulty reducing data uncertainty while shedding no light on model transparency. To this end, a novel adv...
Saved in:
| Main Authors: | Limin Mao, Hangming Shi, Yongxin Chou, Jinliang Cong, Mingli Lu, Zekang Bian, Suhang Gu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01928-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
DIA-TSK: A Dynamic Incremental Adaptive Takagi–Sugeno–Kang Fuzzy Classifier
by: Hao Chen, et al.
Published: (2025-03-01) -
Classification algorithm for imbalance data of ECG based on PSOFS and TSK fuzzy system
by: Xinhui LI, et al.
Published: (2022-09-01) -
Proposing a Fuzzy Soft‐max‐based classifier in a hybrid deep learning architecture for human activity recognition
by: Reza Shakerian, et al.
Published: (2022-03-01) -
Assessment of Scientific Creative-Potential by Near-Infrared Spectroscopy Using Brain-Network-Based Deep-Fuzzy Classifier
by: Sayantani Ghosh, et al.
Published: (2025-01-01) -
Prediction of High-Risk Cardiac Arrhythmia Based on Optimized Deep Active Learning
by: Homeyra Amiri, et al.
Published: (2025-01-01)