Meta-Interpretive Learning from noisy images

Statistical machine learning is widely used in image classification. However, most techniques (1) require many images to achieve high accuracy and (2) do not provide support for reasoning below the level of classification, and so are unable to support secondary reasoning, such as the existence and p...

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Vydáno v:	Machine learning Ročník 107; číslo 7; s. 1097 - 1118
Hlavní autoři:	Muggleton, Stephen, Dai, Wang-Zhou, Sammut, Claude, Tamaddoni-Nezhad, Alireza, Wen, Jing, Zhou, Zhi-Hua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.07.2018 Springer Nature B.V
Témata:	Accuracy Artificial Intelligence Artificial neural networks Computer Science Concavity Control Convexity Image classification Image contrast Light reflection Light sources Logic programming Logic programs Machine learning Mechatronics Natural Language Processing (NLP) Noise Reasoning Robotics Simulation and Modeling Special Issue of the Inductive Logic Programming (ILP) 2016 Support vector machines Two dimensional models Inductive logic programming High-level vision Robocup Meta-interpretive learning
ISSN:	0885-6125, 1573-0565
On-line přístup:	Získat plný text
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