L
Lars Buitinck
Researcher at University of Amsterdam
Publications - 9
Citations - 1905
Lars Buitinck is an academic researcher from University of Amsterdam. The author has contributed to research in topics: Python (programming language) & Application programming interface. The author has an hindex of 7, co-authored 9 publications receiving 1207 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.
Journal ArticleDOI
Scikit-learn: Machine Learning Without Learning the Machinery
TL;DR: A quick introduction to scikit-learn as well as to machine-learning basics are given.
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.
Book ChapterDOI
Multi-emotion Detection in User-Generated Reviews
TL;DR: A new dataset of user-generated movie reviews annotated for emotional expressions is described, and two algorithms that can detect multiple emotions in each sentence of these reviews are experimentally validated.
Proceedings ArticleDOI
Linking the kingdom: enriched access to a historiographical text
TL;DR: This paper presents a method for connecting a historiographical text to the Linked Data cloud, and presents two sources of structured knowledge that link to individual text sources, retrievable on the Web of Data.