mvlearn: Multiview Machine Learning in Python']Python

被引：0

作者：

Perry, Ronan ^{[1
]}

Mischler, Gavin ^{[10
]}

Guo, Richard ^{[2
]}

Lee, Theodore ^{[1
]}

Chang, Alexander ^{[1
]}

Koul, Arman ^{[1
]}

Franz, Cameron ^{[2
]}

Richard, Hugo ^{[5
]}

Carmichael, Iain ^{[6
]}

Ablin, Pierre ^{[7
,8
]}

Gramfort, Alexandre ^{[5
]}

Vogelstein, Joshua T. ^{[1
,3
,4
,9
]}

机构：

[1] Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD 21218 USA

[2] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA

[3] Johns Hopkins Univ, Ctr Imaging Sci, Baltimore, MD 21218 USA

[4] Johns Hopkins Univ, Kavli Neurosci Discovery Inst, Inst Computat Med, Baltimore, MD 21218 USA

[5] Univ Paris Saclay, INRIA, Palaiseau, France

[6] Univ Washington, Dept Stat, Seattle, WA 98195 USA

[7] PSL Univ, Ecole Normale Super, CNRS, Paris, France

[8] PSL Univ, Ecole Normale Super, DMA, Paris, France

[9] Progress Learning, Baltimore, MD 21218 USA

[10] Columbia Univ, Dept Elect Engn, New York, NY 10027 USA

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2021年 / 22卷

关键词：

multiview; machine learning; !text type='python']python[!/text; multi-modal; multi-table; multi-block; CANONICAL CORRELATION-ANALYSIS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have grown in recent years. However, no comprehensive package exists that enables non-specialists to use these methods easily. mvlearn is a Python library which implements the leading multiview machine learning methods. Its simple API closely follows that of scikit-learn for increased ease-of-use. The package can be installed from Python Package Index (PyPI) and the conda package manager and is released under the MIT open-source license. The documentation, detailed examples, and all releases are available at https://mvlearn.github.io/.

引用

页数：7

共 50 条

[1] mvlearn: Multiview machine learning in python
Perry, Ronan
Mischler, Gavin
Guo, Richard
Lee, Theodore
Chang, Alexander
Koul, Arman
Franz, Cameron
Richard, Hugo
Carmichael, Iain
Ablin, Pierre
Gramfort, Alexandre
Vogelstein, Joshua T.
Journal of Machine Learning Research, 2021, 22
[2] An Introduction to Machine Learning in Python']Python
Clevert, D. -A.
TOXICOLOGY LETTERS, 2023, 384 : S5 - S5
[3] Machine Learning in Python']Python Fundamentals
Hernandez Aguilar, Jose Alberto
Hernandez Perez, Yasmin
2023 12TH INTERNATIONAL CONFERENCE ON SOFTWARE PROCESS IMPROVEMENT, CIMPS 2023, 2023, : 281 - 284
[4] Python']Python Machine Learning Case Studies
Puerta Monsalve, Harry L.
CUADERNO ACTIVA, 2018, (10):
[5] Machine learning using Stata/Python']Python
Cerulli, Giovanni
STATA JOURNAL, 2022, 22 (04): : 772 - 810
[6] Machine Learning in Python']Python with No Strings Attached
Baudart, Guillaume
Hirzel, Martin
Kate, Kiran
Mandel, Louis
Shinnar, Avraham
PROCEEDINGS OF THE 3RD ACM SIGPLAN INTERNATIONAL WORKSHOP ON MACHINE LEARNING AND PROGRAMMING LANGUAGES (MAPL '19), 2019, : 1 - 9
[7] Machine Learning Estimators: Implementation and Comparison in Python']Python
Merle, Fabian
COMPUTATIONAL METHODS IN APPLIED MATHEMATICS, 2025, 25 (01) : 153 - 171
[8] Scikit-learn: Machine Learning in Python']Python
Pedregosa, Fabian
Varoquaux, Gaeel
Gramfort, Alexandre
Michel, Vincent
Thirion, Bertrand
Grisel, Olivier
Blondel, Mathieu
Prettenhofer, Peter
Weiss, Ron
Dubourg, Vincent
Vanderplas, Jake
Passos, Alexandre
Cournapeau, David
Brucher, Matthieu
Perrot, Matthieu
Duchesnay, Edouard
JOURNAL OF MACHINE LEARNING RESEARCH, 2011, 12 : 2825 - 2830
[9] SPAM: Simplifying Python']Python for Approaching Machine Learning
Rosiene, Joel A.
Rosiene, Carolyn Pe
2020 IEEE FRONTIERS IN EDUCATION CONFERENCE (FIE 2020), 2020,
[10] River: machine learning for streaming data in Python']Python
Montiel, Jacob
Halford, Max
Mastelini, Saulo Martiello
Bolmier, Geoffrey
Sourty, Raphael
Vaysse, Robin
Zouitine, Adil
Gomes, Heitor Murilo
Read, Jesse
Abdessalem, Talel
Bifet, Albert
JOURNAL OF MACHINE LEARNING RESEARCH, 2021, 22

← 1 2 3 4 5 →