2021

Frugal Machine Learning

Evchenko, Mikhail, Vanschoren, Joaquin, Hoos, Holger H. et al.

Understand

Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet of Things.

In most machine learning applications, the main focus is on the quality of the results achieved (e.g., prediction accuracy), and hence vast amounts of data are being collected, requiring significant computational resources to build models.
In many scenarios, however, it is infeasible or impractical to set up large centralized data repositories.
In personal health, for instance, privacy issues may inhibit the sharing of detailed personal data.

Built on

Algorithm as 136: A k-means clustering algorithm
John A Hartigan and Manchek A Wong · 1979
Earlier work this paper cites.
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Peter J Rousseeuw · 1987
Earlier work this paper cites.
Partitioning around medoids (program pam)
Leonard Kaufman and Peter J Rousseeuw · 1990
Earlier work this paper cites.
Optimal brain damage
Yann LeCun, John S. Denker, and Sara A. Solla · 1990
Earlier work this paper cites.
A training algorithm for optimal margin classifiers
B. Boser, I. Guyon, and V. Vapnik · 1992
Earlier work this paper cites.
Bdd based decomposition of logic functions with application to fpga synthesis
Yung-Te Lai, Massoud Pedram, and Sarma B. K. Vrudhula · 1993
Earlier work this paper cites.
The use of the area under the roc curve in the evaluation of machine learning algorithms
Andrew P Bradley · 1997
Earlier work this paper cites.
Binary decision diagrams
Shin-ichi Minato and Saburo Muroga · 1999
Earlier work this paper cites.
Mining high-speed data streams
Pedro Domingos and Geoff Hulten · 2000
Earlier work this paper cites.
A simple generalisation of the area under the roc curve for multiple class classification problems
David J Hand and Robert J Till · 2001
Earlier work this paper cites.
Gene selection for cancer classification using support vector machines
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik · 2002
Earlier work this paper cites.
Principal component analysis
Ian Jolliffe · 2002
Earlier work this paper cites.
Ranking learning algorithms: Using ibl and meta-learning on accuracy and time results
Pavel B Brazdil, Carlos Soares, and Joaquim Pinto Da Costa · 2003
Earlier work this paper cites.

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Then

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Openml: networked science in machine learning
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Deep learning in neural networks: An overview
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