R
Robert Layton
Researcher at Federation University Australia
Publications - 50
Citations - 2417
Robert Layton is an academic researcher from Federation University Australia. The author has contributed to research in topics: Phishing & The Internet. The author has an hindex of 17, co-authored 50 publications receiving 1788 citations.
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API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck,Gilles Louppe,Mathieu Blondel,Fabian Pedregosa,Andreas Mueller,Olivier Grisel,Vlad Niculae,Peter Prettenhofer,Alexandre Gramfort,Jaques Grobler,Robert Layton,Jake Vanderplas,Arnaud Joly,Brian Holt,Gaël Varoquaux +14 more
TL;DR: Scikit-learn as mentioned in this paper is a machine learning library written in Python, which is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts.
Proceedings Article
API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck,Gilles Louppe,Mathieu Blondel,Fabian Pedregosa,Andreas Mueller,Olivier Grisel,Vlad Niculae,Peter Prettenhofer,Alexandre Gramfort,Jaques Grobler,Robert Layton,Jake Vanderplas,Arnaud Joly,Brian Holt,Gaël Varoquaux +14 more
TL;DR: Scikit-learn as discussed by the authors is a machine learning library written in Python, which is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts.
Proceedings ArticleDOI
Authorship Attribution for Twitter in 140 Characters or Less
TL;DR: It is shown that the SCAP methodology performs extremely well on twitter messages and even with restrictions on the types of information allowed, such as the recipient of directed messages, still perform significantly higher than chance.
Proceedings Article
Malware Detection Based on Structural and Behavioural Features of API Calls
TL;DR: A fully automated system to disassemble and extract API call features effectively from executables and use n-gram statistical analysis of binary content to classify if an executable file is malicious or benign is developed.
Journal ArticleDOI
Automated unsupervised authorship analysis using evidence accumulation clustering
TL;DR: An automated and unsupervised methodology for clustering documents by authorship is proposed, named NUANCE, for n-gram Unsupervised Automated Natural Cluster Ensemble, indicating that the derived clusters have a strong correlation to the true authorship of unseen documents.