A Survey on Scenario Theory, Complexity, and Compression-Based Learning and Generalization-Reference-Cited by-同舟云学术

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A Survey on Scenario Theory, Complexity, and Compression-Based Learning and Generalization

Published:2023 Issue: Volume: Page:1-15
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Rocchetta Roberto¹^ORCID,Mey Alexander²^ORCID,Oliehoek Frans A.³^ORCID

Affiliation:

1. Dipartimento ambiente costruzioni e design (DACD)-Istituto sostenibilità applicata all’ambiente costruito (ISAAC), Intelligent Energy Systems Team, University of Applied Sciences and Arts of Southern Switzerland (SUPSI), Mendrisio, Switzerland

2. Department of Mathematics and Computer Science, Technical University of Eindhoven, Eindhoven, The Netherlands

3. Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands

Funder

H2020-ECSEL

European Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10250820.pdf?arnumber=10250820

Cited by 2 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. A survey on LED Prognostics and Health Management and uncertainty reduction;Microelectronics Reliability;2024-06

2. Design of Robust PID Controllers Based on the Scenario Approach;2023 IEEE Third International Conference on Signal, Control and Communication (SCC);2023-12-01

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