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Cuvillier Verlag

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Internationaler Fachverlag für Wissenschaft und Wirtschaft

Cuvillier Verlag

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Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems

Printausgabe
EUR 56,10

E-Book
EUR 39,30

Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems (Band 101)

Schahin Tofangchi (Autor)

Vorschau

Inhaltsverzeichnis, PDF (530 KB)
Leseprobe, PDF (1,1 MB)

ISBN-13 (Printausgabe) 9783736972001
ISBN-13 (E-Book) 9783736962002
Sprache Englisch
Seitenanzahl 202
Auflage 1.
Buchreihe Göttinger Wirtschaftsinformatik
Band 101
Erscheinungsort Göttingen
Promotionsort Göttingen
Erscheinungsdatum 21.04.2020
Allgemeine Einordnung Dissertation
Fachbereiche Wirtschaftswissenschaften
Informatik
Schlagwörter maschinelles Lernen, künstliche Intelligenz, Big-Data-Analytics, Expertensysteme, verteiltes maschinelles Lernen, Online-Learning, autonome Agenten, medizinische Entscheidungsunterstützungssysteme, personalisierte Systeme, machine learning, artificial intelligence, big data analytics, expert system, distributed machine learning, online learning, autonomous agents, medical decision support systems, personalized systems, Research Agenda, Motivation, Consumer-Centric, Framework, Domain-Specific, Environment, Percepts, Actuators, Forschungsagenda, Motivation, Verbraucherzentriert, Rahmenwerk, Bereichsspezifisch, Umwelt, Wahrnehmungen, Stellantriebe
URL zu externer Homepage https://www.uni-goettingen.de/de/schahin+tofangchi/549620.html
Beschreibung

The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the challenges of big data but also leverage their characteristics to perform more sophisticated analyses. I evaluate the proposed design principles in three practical settings, in which I apply the principles to design machine learning systems that (i) support treatment decision making for cancer patients, (ii) provide consumers with recommendations on two-sided platforms, and (iii) address a trade-off between efficiency and comfort in the context of autonomous vehicles. The evaluations partially validate the proposed theory, but also show that some principles require further attention in order to be practicable.