Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Lucas Baier"'
Publikováno v:
Cognitive Systems Research. 76:78-92
The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provides an answer to how learning performance differs between humans and ma
Publikováno v:
Journal of Service Management. 32:265-288
PurposeWhile the understanding of customer satisfaction is a key success factor for service enterprises, existing elicitation approaches suffer from several drawbacks such as high manual effort or delayed availability. However, the rise of analytical
Publikováno v:
Lecture Notes in Networks and Systems ISBN: 9783030821920
IntelliSys (1)
IntelliSys (1)
The importance of deployed machine learning solutions has increased significantly in the past years due to the availability of data sources, computing capabilities and convenient tooling. However, technical challenges such as limited resources and co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56cbea4230026d014e889447b2cf59ca
https://doi.org/10.1007/978-3-030-82193-7_57
https://doi.org/10.1007/978-3-030-82193-7_57
Publikováno v:
Communications of the Association for Information Systems, 48, 589-615
Within the last decade, the application of supervised machine learning (SML) has become increasingly popular in the field of information systems (IS) research. Although the choices among different data preprocessing techniques, as well as different a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4020a92f9302bb3aa15bcb7418597d67
https://publikationen.bibliothek.kit.edu/1000124438
https://publikationen.bibliothek.kit.edu/1000124438
Publikováno v:
HICSS
Machine learning models nowadays play a crucial role for many applications in business and industry. However, models only start adding value as soon as they are deployed into production. One challenge of deployed models is the effect of changing data
Publikováno v:
CBI (1)
Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift. Prediction quality of models can be larg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3111fbed227d151c460bd28b339daea3
Publikováno v:
Wirtschaftsinformatik (Zentrale Tracks)
Machine learning models are omnipresent for predictions on big data. One challenge of deployed models is the change of the data over time, a phenomenon called concept drift. If not handled correctly, a concept drift can lead to significant mispredict
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3fb28cbe7b5b6f2a686a5606cbdaed9
https://publikationen.bibliothek.kit.edu/1000105012
https://publikationen.bibliothek.kit.edu/1000105012
Publikováno v:
HICSS
Companies more and more rely on predictive services which are constantly monitoring and analyzing the available data streams for better service offerings. However, sudden or incremental changes in those streams are a challenge for the validity and pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7137aca3ed91f666e6dc6cce59dee48e
Publikováno v:
RTSS
Embedded systems are increasingly required to handle code of various qualities that must often be isolated, yet predictably share resources. This has motivated the isolation of, for example, mission-critical code from best-effort features using isola