Výsledky vyhledávání

How to Conduct Rigorous Supervised Machine Learning in Information Systems Research: The Supervised Machine Learning Reportcard

Autor: Gerhard Satzger, Björn Schmitz, Niklas Kühl, Robin Hirt, Lucas Baier

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

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Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability

Autor: Carlos Berg, Niklas Kühl, Akash Srivastava, Robin Hirt

Publikováno v: HICSS

In networks of independent entities that face similar predictive tasks, transfer machine learning enables to re-use and improve neural nets using distributed data sets without the exposure of raw data. As the number of data sets in business networks

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::022595d30765547b2507b82ea575517e
https://doi.org/10.24251/hicss.2021.851

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How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting

Autor: Robin Hirt, Niklas Kühl, Gerhard Satzger, Yusuf Peker

Publikováno v: CBI (1)

In a variety of business situations, the introduction or improvement of machine learning approaches is impaired as these cannot draw on existing analytical models. However, in many cases similar problems may have already been solved elsewhere-but the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d731736db4bf750c610295511a12b8ca
http://arxiv.org/abs/2005.10698

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A Transfer Machine Learning Matching Algorithm for Source and Target (TL-MAST)

Autor: Robin Hirt, Florin Tim Peters

Publikováno v: Machine Learning, Optimization, and Data Science ISBN: 9783030645793
LOD (2)

Sequentially transferring machine learning (ML) models in a row over several data sets can result in an improvement of performance. Considering an increasing number of data sets, the possible number “transfer paths” of a transfer grows exponentia

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::69459ac59498aa1fcb813cfda0e0102d
https://doi.org/10.1007/978-3-030-64580-9_45

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Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime

Autor: Niklas Kühl, Robin Hirt, Jannis Walk, Erik R. Hersløv

Publikováno v: Exploring Service Science ISBN: 9783030387235
IESS

Reverse Vending Machines (RVMs) are a proven instrument for facilitating closed-loop plastic packaging recycling. A good customer experience at the RVM is crucial for a further proliferation of this technology. Bin full events are the major reason fo

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Machine Learning in Artificial Intelligence: Towards a Common Understanding

Autor: Niklas Kühl, Robin Hirt, Marc Goutier, Gerhard Satzger

Publikováno v: HICSS

The application of "machine learning" and "artificial intelligence" has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we aim to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba60caae9de36aac0b3d6f5f1332a1ed

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