Treffer: Efficient Machine Learning for Data Streams in Python

Title:
Efficient Machine Learning for Data Streams in Python
Contributors:
Sorbonne Université (SU), Centre National de la Recherche Scientifique (CNRS), Recherche Opérationnelle (RO), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Publisher Information:
CCSD
Publication Year:
2025
Document Type:
other/unknown material
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/arxiv/2502.07432; ARXIV: 2502.07432
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edsbas.A6C9BF0D
Database:
BASE

Weitere Informationen

ARXIV ; CapyMOA is an open-source library designed for efficient machine learning on streaming data. It provides a structured framework for real-time learning and evaluation, featuring a flexible data representation. CapyMOA includes an extensible architecture that allows integration with external frameworks such as MOA and PyTorch, facilitating hybrid learning approaches that combine traditional online algorithms with deep learning techniques. By emphasizing adaptability, scalability, and usability, CapyMOA allows researchers and practitioners to tackle dynamic learning challenges across various domains.