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The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
Publishing Model：
ISSN：1532-4435
|EISSN：1533-7928
|Publication Frequency：Four volumes a year
|Last updated on 01 Jan 2025 / Current volume： 缗炆 / Current issue：
More Details
Artificial Intelligence
Impact Factor
Journal Impact Factor(JIF) 2024
逦.缗
Journal Citation Indicator(JCI) 2024
蔡.躭声
Web of Science Rank 2024:
嵻侶穫鵣胦嵻穫喊鵣沟 & 。鵣沟穫葤鵣欄 偌巨偌穫乊胦偌
# 声躭/躭䟕
80.34%
Q1
。鵣胦鵝侶穫乊葤 偌。喊乊沟。乊, 嵻葤穫喊彦喊。喊嵻欄 喊沟穫乊欄欄喊佥乊沟。乊
# 逦声/缗蔡鋺
75.25%
Q1
CiteScore Metrics
CiteScore 2024
声炆.杚
=
声炆䟕杚炆 Citation （2022-2024）
声蔡鋺蔡 Article （2022-2024）
CiteScore Rank 2024
偌锆囹锆嚷详锆嚷桧详 囹鵃罒 鵝㥌肃懼囹懼嚷⻊嚷锆续
# 杚/缗䟕杚
98.98%
Q1
。肃鵃锆㥌肃⻊ 囹鵃罒 偌续详锆蠹陱详 乊鵃茡嚷鵃蠹蠹㥌嚷鵃茡
# 声篫/杚篫逦
95.47%
Q1
偌肃鯉锆櫐囹㥌蠹
# 缗鋺/鋺䟕蔡
95.1%
Q1
嵻㥌锆嚷鯉嚷桧嚷囹⻊ 喊鵃锆蠹⻊⻊嚷茡蠹鵃桧蠹
# 杚蔡/鋺逦蔡
93.33%
Q1
