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info.json
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/
info.json
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{
"abstract": "scikit-multimodallearn is a Python library for multimodal supervised learning, licensed under Free BSD, and compatible with the well-known scikit-learn toolbox (Fabian Pedregosa, 2011). This paper details the content of the library, including a specific multimodal data formatting and classification and regression algorithms. Use cases and examples are also provided.",
"authors": [
"Dominique Benielli",
"Baptiste Bauvin",
"Sokol Ko\u00e7o",
"Riikka Huusari",
"C\u00e9cile Capponi",
"Hachem Kadri",
"Fran\u00e7ois Laviolette"
],
"emails": [
"dominique.benielli@univ-amu.fr",
"baptiste.bauvin@lis-lab.fr",
"sokol.koco@mines-stetienne.fr",
"riikka.huusari@aalto.fi",
"cecile.capponi@lis-lab.fr",
"hachem.kadri@lis-lab.fr",
"francois.laviolette@ift.ulvavl.ca"
],
"extra_links": [
[
"code",
"https://github.com/dbenielli/scikit-multimodallearn"
]
],
"id": "21-0791",
"issue": 51,
"pages": [
1,
7
],
"special_issue": "MLOSS",
"title": "Toolbox for Multimodal Learn (scikit-multimodallearn)",
"volume": 23,
"year": 2022
}