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Overview
Machine Learning is an international forum focusing on computational approaches to learning.
- Reports substantive results on a wide range of learning methods applied to various learning problems.
- Provides robust support through empirical studies, theoretical analysis, or comparison to psychological phenomena.
- Demonstrates how to apply learning methods to solve significant application problems.
- Improves how machine learning research is conducted.
- Prioritizes verifiable and replicable supporting evidence in all published papers.
- Editor-in-Chief
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- Hendrik Blockeel
- Impact factor
- 7.5 (2022)
- 5 year impact factor
- 6.3 (2022)
- Submission to first decision (median)
- 28 days
- Downloads
- 1,349,126 (2023)
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Latest articles
Journal updates
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CfP: Discovery Science 2023
Submission Deadline: March 4, 2024
Guest Editors: Rita P. Ribeiro, Albert Bifet, Ana Carolina Lorena
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Call for Papers: Conformal Prediction and Distribution-Free Uncertainty Quantification
Submission Deadline: January 7th, 2024
Guest Editors: Henrik Boström, Eyke Hüllermeier, Ulf Johansson, Khuong An Nguyen, Aaditya Ramdas
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Call for Papers: DSAA 2024 Journal Track with Machine Learning Journal
Guest Editors: Longbing Cao, David C. Anastasiu, Qi Zhang, Xiaolin Huang,
Journal information
- Electronic ISSN
- 1573-0565
- Print ISSN
- 0885-6125
- Abstracted and indexed in
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- ACM Digital Library
- ANVUR
- BFI List
- Baidu
- CLOCKSS
- CNKI
- CNPIEC
- Current Contents/Engineering, Computing and Technology
- DBLP
- Dimensions
- EBSCO
- EI Compendex
- Google Scholar
- INSPEC
- Japanese Science and Technology Agency (JST)
- Mathematical Reviews
- Naver
- OCLC WorldCat Discovery Service
- Portico
- ProQuest
- SCImago
- SCOPUS
- Science Citation Index Expanded (SCIE)
- TD Net Discovery Service
- UGC-CARE List (India)
- Wanfang
- zbMATH
- Copyright information
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