Peter Reutemann

According to our database, Peter Reutemann authored at least 10 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Hyperspectral Imaging and Machine Learning to Identify Epicuticular Wax Loss in Masena Blueberries for Post-Harvest Freshness.

Jonathan Pearse, Yaminn Thawdar, Alicia Sim, Melanie Ooi, Benjamin McGuinness, Peter Reutemann, Dale Fletcher, Mike Duke

Proceedings of the 20th IEEE International Conference on Automation Science and Engineering, 2024

2016

MEKA: A Multi-label/Multi-target Extension to WEKA.

Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes

J. Mach. Learn. Res., 2016

2015

Big Data with ADAMS.

Peter Reutemann, Geoff Holmes

Proceedings of the 4th International Workshop on Big Data, 2015

2012

Scientific Workflow Management with ADAMS.

Peter Reutemann, Joaquin Vanschoren

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

2010

WEKA - Experiences with a Java Open-Source Project.

Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten

J. Mach. Learn. Res., 2010

2009

The WEKA data mining software: an update.

Mark A. Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten

SIGKDD Explor., 2009

Analysing chromatographic data using data mining to monitor petroleum content in water.

Geoffrey Holmes, Dale Fletcher, Peter Reutemann, Eibe Frank

Proceedings of the Information Technologies in Environmental Engineering, 2009

2007

Scaling Up Semi-supervised Learning: An Efficient and Effective LLGC Variant.

Bernhard Pfahringer, Claire Leschi, Peter Reutemann

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2007

2006

Using Weighted Nearest Neighbor to Benefit from Unlabeled Data.

Kurt Driessens, Peter Reutemann, Bernhard Pfahringer, Claire Leschi

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2006

2004

A Toolbox for Learning from Relational Data with Propositional and Multi-instance Learners.

Peter Reutemann, Bernhard Pfahringer, Eibe Frank

Proceedings of the AI 2004: Advances in Artificial Intelligence, 2004