A Meta Learning Approach for Automating Model Selection in Big Data Environments using Microservice and Container Virtualization Technologies-Reference-Cited by-同舟云学术

A Meta Learning Approach for Automating Model Selection in Big Data Environments using Microservice and Container Virtualization Technologies

Published:2020-11-02 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th International Conference on Management of Digital EcoSystems
language:
Short-container-title:

Author:

Shahoud Shadi¹,Khalloof Hatem¹,Winter Moritz¹,Duepmeier Clemens¹,Hagenmeyer Veit¹

Affiliation:

1. Karlsruhe Institute of Technology, IAI, Karlsruhe, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3415958.3433072

Reference31 articles.

1. Characterizing the applicability of classification algorithms using meta-level learning

2. Can Cui Teresa Wu Mengqi Hu Jeffery D Weir and Xiwang Li. 2016. Shortterm building energy model recommendation system: A meta-learning approach. Applied energy 172 (2016) 251--263. Can Cui Teresa Wu Mengqi Hu Jeffery D Weir and Xiwang Li. 2016. Shortterm building energy model recommendation system: A meta-learning approach. Applied energy 172 (2016) 251--263.

3. Clemens Duepmeier and Veit Hagenmeyer. 2020. Facilitating and Managing Machine Learning and Data Analysis Tasks in Big Data Environments Using Web and Microservice Technologies. Transactions on Large-Scale Data-and Knowledge-Centered Systems XLV: Special Issue on Data Management and Knowledge Extraction in Digital Ecosystems 12390 (2020) 132. Clemens Duepmeier and Veit Hagenmeyer. 2020. Facilitating and Managing Machine Learning and Data Analysis Tasks in Big Data Environments Using Web and Microservice Technologies. Transactions on Large-Scale Data-and Knowledge-Centered Systems XLV: Special Issue on Data Management and Knowledge Extraction in Digital Ecosystems 12390 (2020) 132.

4. Matthias Feurer Aaron Klein Katharina Eggensperger Jost Springenberg Manuel Blum and Frank Hutter. 2015. Efficient and robust automated machine learning. In Advances in neural information processing systems. 2962--2970. Matthias Feurer Aaron Klein Katharina Eggensperger Jost Springenberg Manuel Blum and Frank Hutter. 2015. Efficient and robust automated machine learning. In Advances in neural information processing systems. 2962--2970.

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