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Handling Data Problems in Machine Learning Applications in Supply Chain Management.

Name: Handling Data Problems in Machine Learning Applications in Supply Chain Management.
Brand: Fraunhofer Verlag
SKU: 9783839617861
Price: 57.28 EUR
Availability: InStock

A Multiple-Case Study on the Analysis of Data Augmentation Approaches.. Dissertationsschrift

Autor: Christian Menden, Alexander Pflaum, Fraunhofer-Arbeitsgruppe für Supply Chain Services des Fraunhofer IIS

Jazyk:

Väzba: Brožovaná

Vydavateľ: Fraunhofer Verlag

Dostupnosť: 50 % šanca

Prehľadáme celý svet

57.28 €

In recent years, considerable progress has been made in research on artificial intelligence, particu...

Informácie o knihe

Autor

Christian Menden, Alexander Pflaum, Fraunhofer-Arbeitsgruppe für Supply Chain Services des Fraunhofer IIS

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2022

Stránok

365

EAN

9783839617861

Enbook ID

38831631

Vydavateľ

Fraunhofer Verlag

Hmotnosť

620

Rozmery

170 x 20 x 239

Kompletný popis

In recent years, considerable progress has been made in research on artificial intelligence, particularly in the sub-area of machine learning (ML) where information is extracted from huge data sets. In practice, however, the existing data is often dirty, erroneous, not available in sufficient quantity, or does not meet the requirements for a direct application of ML methods. Against this background, data augmentation (DA) methods can be used to improve the data quality with the aim of enabling an initial application of ML methods or improving the results of existing ML models. Today, there is a wide range of different DA methods, which makes it oftentimes difficult to select an appropriate DA method for a particular application. Further, it remains unclear what the potential benefits and possible obstacles are to using DA for ML methods in practice. In this regard, this dissertation aims to contribute to a better understanding of DA and to demonstrate, by means of a multiple-case study, how DA can improve the performance and applicability of ML methods in the context of supply chain management.

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Handling Data Problems in Machine Learning Applications in Supply Chain Management.

A Multiple-Case Study on the Analysis of Data Augmentation Approaches.. Dissertationsschrift

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