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

Name: Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems
Brand: Cuvillier
SKU: 9783736972001
Price: 48.55 EUR
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Autor: Schahin Tofangchi

Jazyk:

Väzba: Brožovaná

Vydavateľ: Cuvillier

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48.55 €

The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new...

Informácie o knihe

Autor

Schahin Tofangchi

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2020

Stránok

202

EAN

9783736972001

ISBN

3736972008

Enbook ID

32741991

Vydavateľ

Cuvillier

Hmotnosť

245

Rozmery

148 x 210 x 12

Kompletný popis

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.

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

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