Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2018 IEEE International Conference on Services Computing (SCC) |
| Herausgeber (Verlag) | Institution of Electrical Engineers (IEE) |
| Seiten | 241-244 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 978-1-5386-7250-1 |
| ISBN (Print) | 978-1-5386-7251-8 |
| Publikationsstatus | Veröffentlicht - 2018 |
| Extern publiziert | Ja |
| Veranstaltung | 2018 IEEE International Conference on Services Computing (SCC) - San Francisco, USA / Vereinigte Staaten Dauer: 2 Juli 2018 → 7 Juli 2018 |
Abstract
Automated service composition as the process of creating new software in an automated fashion has been studied in many different ways over the last decade. However, the impact of automated service composition has been rather small as its utility in real-world applications has not been demonstrated so far. This paper describes the use case of automated machine learning, a real-world scenario in which automated service composition plays an important role. It turns out that most existing service composition approaches are not able to reasonably solve this problem, because it requires to evaluate candidates by executing them during search. We briefly sketch a new service composition algorithm, MLS-PLAN, and illustrate how it can be applied to the problem of automated machine learning.
ASJC Scopus Sachgebiete
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
- Informatik (insg.)
- Hardware und Architektur
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Angewandte Informatik
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2018 IEEE International Conference on Services Computing (SCC). Institution of Electrical Engineers (IEE), 2018. S. 241-244 8456425.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Towards the Automated Composition of Machine Learning Services
AU - Mohr, Felix
AU - Wever, Marcel
AU - Hüllermeier, Eyke
AU - Faez, Amin
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - Automated service composition as the process of creating new software in an automated fashion has been studied in many different ways over the last decade. However, the impact of automated service composition has been rather small as its utility in real-world applications has not been demonstrated so far. This paper describes the use case of automated machine learning, a real-world scenario in which automated service composition plays an important role. It turns out that most existing service composition approaches are not able to reasonably solve this problem, because it requires to evaluate candidates by executing them during search. We briefly sketch a new service composition algorithm, MLS-PLAN, and illustrate how it can be applied to the problem of automated machine learning.
AB - Automated service composition as the process of creating new software in an automated fashion has been studied in many different ways over the last decade. However, the impact of automated service composition has been rather small as its utility in real-world applications has not been demonstrated so far. This paper describes the use case of automated machine learning, a real-world scenario in which automated service composition plays an important role. It turns out that most existing service composition approaches are not able to reasonably solve this problem, because it requires to evaluate candidates by executing them during search. We briefly sketch a new service composition algorithm, MLS-PLAN, and illustrate how it can be applied to the problem of automated machine learning.
KW - Hierarchical planning
KW - Machine learning
KW - automated service composition
UR - http://www.scopus.com/inward/record.url?scp=85050925693&partnerID=8YFLogxK
U2 - 10.1109/SCC.2018.00039
DO - 10.1109/SCC.2018.00039
M3 - Conference contribution
SN - 978-1-5386-7251-8
SP - 241
EP - 244
BT - 2018 IEEE International Conference on Services Computing (SCC)
PB - Institution of Electrical Engineers (IEE)
T2 - 2018 IEEE International Conference on Services Computing (SCC)
Y2 - 2 July 2018 through 7 July 2018
ER -