Automated Machine Learning Overview

Budjač Roman (), Nikmon Marcel (), Schreiber Peter (), Zahradníková Barbora () and Janáčová Dagmar ()
Additional contact information
Budjač Roman: 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 24Trnava, Slovak Republic
Nikmon Marcel: 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 24Trnava, Slovak Republic
Schreiber Peter: 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 24Trnava, Slovak Republic
Zahradníková Barbora: 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 24Trnava, Slovak Republic
Janáčová Dagmar: Tomas Bata University of Zlin, Faculty of Applied Informatics, Department of Automation and Control Engineering, Nad Stráněmi 4511, 760 05Zlín, Czech Republic

Research Papers Faculty of Materials Science and Technology Slovak University of Technology, 2019, vol. 27, issue 45, 107-112

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.

Keywords: Neural networks; auto-machine learning; deep neural network (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/rput-2019-0033 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:repfms:v:27:y:2019:i:45:p:107-112:n:15

DOI: 10.2478/rput-2019-0033

Access Statistics for this article

Research Papers Faculty of Materials Science and Technology Slovak University of Technology is currently edited by Kvetoslava Rešetová

More articles in Research Papers Faculty of Materials Science and Technology Slovak University of Technology from Sciendo
Bibliographic data for series maintained by Peter Golla ().