On the Diversity of Machine Learning Models for System Reliability-Reference-Cited by-同舟云学术

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On the Diversity of Machine Learning Models for System Reliability

Published:2019-12 Issue: Volume: Page:
ISSN:
Container-title:2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC)
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Author:

Machida Fumio

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8938608/8952108/08952144.pdf?arnumber=8952144

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploiting Transformer Models in Three-Version Image Classification Systems;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

2. Reliability and Performance Evaluation of Two-input Machine Learning Systems;2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC);2023-10-24

3. Characterizing Reliability of Three-version Traffic Sign Classifier System through Diversity Metrics;2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE);2023-10-09

4. Bayesian Network Reliability Modeling for Three-version Machine Learning Systems;2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S);2023-06

5. How Data Diversification Benefits the Reliability of Three-version Image Classification Systems;2022 IEEE 27th Pacific Rim International Symposium on Dependable Computing (PRDC);2022-11

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