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Yu-Cheng Chang

15 papers hereh-index 16821 citations51 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author13

Across the 13 of 15 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.AI3
  • cs.MA3
  • cs.HC2
  • cs.CL1
  • cs.RO1
same name
  • Yu-Cheng Chang — 5 papers, h 8
  • Yu-Cheng Chang — 4 papers, h 39
  • Yu-Cheng Chang — 2 papers, h 3
  • Yu-Cheng Chang — 1 paper, h 4
  • Yu-Cheng Chang — 1 paper, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182025
most citedHierarchical fuzzy neural networks with privacy preservation for heterogeneous big data

37 citations · 89 across the 15 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024

A Self-Constructing Multi-Expert Fuzzy System for High-dimensional Data Classification

Yingtao Ren, Yu-Cheng Chang, Thomas Do +2

Fuzzy Neural Networks (FNNs) are effective machine learning models for classification tasks, commonly based on the Takagi-Sugeno-Kang (TSK) fuzzy system. However, when faced with h…

cs.LG2024

Masked EEG Modeling for Driving Intention Prediction

Jinzhao Zhou, Justin Sia, Yiqun Duan +3

Driving under drowsy conditions significantly escalates the risk of vehicular accidents. Although recent efforts have focused on using electroencephalography to detect drowsiness,…

cs.LG2022★ 27 cited

Federated Fuzzy Neural Network with Evolutionary Rule Learning

Leijie Zhang, Ye Shi, Yu-Cheng Chang +1

Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. Howe…

cs.LG2022★ 37 cited

Hierarchical fuzzy neural networks with privacy preservation for heterogeneous big data

Leijie Zhang, Ye Shi, Yu-Cheng Chang +1

Heterogeneous big data poses many challenges in machine learning. Its enormous scale, high dimensionality, and inherent uncertainty make almost every aspect of machine learning dif…

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