Autonomicity Levels and Requirements for Automated Machine Learning-Reference-Cited by-同舟云学术

Autonomicity Levels and Requirements for Automated Machine Learning

Published:2017-09-20 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the International Conference on Research in Adaptive and Convergent Systems
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Author:

Lee Keon Myung¹,Kim Kwang Il¹,Yoo Jaesoo²

Affiliation:

1. Dept. of Computer Science, Chungbuk National University, Cheongju, Korea

2. School of Information and Communication Engineering, Chungbuk National University, Cheonjgu, Korea

Funder

MSIT(Ministry of Science and ICT), Korea,

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3129676.3130241

Reference7 articles.

1. Bergstra J. S. Bardenet R. Bengio Y. Kégl B. 2011. Algorithms for hyper-parameter optimization. In Advances in Neural Information Processing Systems (pp. 2546--2554). Bergstra J. S. Bardenet R. Bengio Y. Kégl B. 2011. Algorithms for hyper-parameter optimization. In Advances in Neural Information Processing Systems (pp. 2546--2554).

2. Density and Frequency-Aware Cluster Identification for Spatio-Temporal Sequence Data

3. Auto-WEKA

4. Lee K.M Lee K.M Lee S.H. 2017. Remote data integrity check for remotely acquired and stored stream data The Journal of Supercomputing. DOI= http://doi.acm.org/10.1007/s11227-017-2117-4. Lee K.M Lee K.M Lee S.H. 2017. Remote data integrity check for remotely acquired and stored stream data The Journal of Supercomputing. DOI= http://doi.acm.org/10.1007/s11227-017-2117-4.

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