An Investigation into Mini-Batch Rule Learning

Florian Beck; Johannes Fürnkranz

An Investigation into Mini-Batch Rule Learning

Institute for Application-oriented Knowledge Processing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings

Abstract

We investigate whether it is possible to learn rule sets efficiently in a network structure with a single hidden layer using iterative refinements over mini-batches of examples. A first rudimentary version shows an acceptable performance on all but one dataset, even though it does not yet reach the performance levels of RIPPER.

Original language	English
Title of host publication	Proceedings of the 2nd Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML)
Editors	Kristian Kersting and Stefan Kramer and Zahra Ahmadi
Number of pages	4
Publication status	Published - 2020

Fields of science

102019 Machine learning
102033 Data mining

JKU Focus areas

Digital Transformation

Cite this

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title = "An Investigation into Mini-Batch Rule Learning",

abstract = "We investigate whether it is possible to learn rule sets efficiently in a network structure with a single hidden layer using iterative refinements over mini-batches of examples. A first rudimentary version shows an acceptable performance on all but one dataset, even though it does not yet reach the performance levels of RIPPER.",

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year = "2020",

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AB - We investigate whether it is possible to learn rule sets efficiently in a network structure with a single hidden layer using iterative refinements over mini-batches of examples. A first rudimentary version shows an acceptable performance on all but one dataset, even though it does not yet reach the performance levels of RIPPER.

M3 - Conference proceedings

BT - Proceedings of the 2nd Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML)

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