Automated Machine Learning Overview-Reference-Cited by-同舟云学术

Automated Machine Learning Overview

Published:2019-09-01 Issue:45 Volume:27 Page:107-112
ISSN:1338-0532
Container-title:Research Papers Faculty of Materials Science and Technology Slovak University of Technology
language:en
Short-container-title:

Author:

Budjač Roman¹,Nikmon Marcel¹,Schreiber Peter¹,Zahradníková Barbora¹,Janáčová Dagmar²

Affiliation:

1. Slovak University of Technology in Bratislava , Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics , Ulica Jána Bottu Č. 25, 917 24 Trnava , Slovak Republic

2. Tomas Bata University of Zlin , Faculty of Applied Informatics, Department of Automation and Control Engineering , Nad Stráněmi 4511, 760 05 Zlín , Czech Republic

Abstract

Abstract This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/rput-2019-0033

Reference19 articles.

1. [1] J.MCHUGH, P. E. CUDDIHY, J. W. WILLIAMS, K. S. AGGOUR, V. S. KUMAR, V. MULWAD. 2017. “Integrated access to big data polystores through a knowledge-driven framework,” 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, pp. 1494-1503. ISBN 978-1-5386-2715-0

2. [2] https://cloud.google.com/solutions/hpc/, High performance computing, [Online]. [Accessed: 09-2019] Available at: https://cloud.google.com/solutions/hpc/

3. [3] https://cloud.google.com/, Cloud AutoML [Online]. [Accessed: 09-2019] Available at: https://cloud.google.com/automl/docs/

4. [4] TUGGENER, L., AMIRIAN, M., ROMBACH, K., LÖRWALD, S., VARLET, A., WESTERMANN, CH., STADELMANN, T. 2019. Automated Machine Learning in Practice: State of the Art and Recent Results. In: 6th Swiss Conference on Data Science (SDS): Bern, Switzerland, pp. 31-36. ISBN 978-1-7281-3105-4.

5. [5] THORNTON, C., HUTTER, F., HOOS, H. H., LEYTON-BROWN, K. 2013. Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms. In: 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ‘13: Chicago, Illinois, USA, pp. 847-855. ISBN 978-1-4503-2174-7

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