Machine Learning

  in Journal   Posted on September 23, 2020

Journal Ranking & Metrics

G2R Score : 5.78
G2R H-Index : 15
JCR Impact Factor : 2.672
Scopus Citescore : 5
SCIMAGO SJR : 1.034
SCIMAGO H-index : 144
Guide2Research Overall Ranking : 132

Journal Information

ISSN : 0885-6125
Publisher :
Periodicity : Monthly
Editors-in-Chief : Hendrik Blockeel
Journal & Submission Website : https://www.springer.com/journal/10994

Top Scientists who published in this Journal

Number of top scientists* : 46
Documents published by top scientists* : 65
* Based on data published during the last three years.

Aims & Scope of the Journal

Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems, including but not limited to: Learning Problems: Classification, regression, recognition, and prediction; Problem solving and planning; Reasoning and inference; Data mining; Web mining; Scientific discovery; Information retrieval; Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and control; Combinatorial optimization; Game playing; Industrial, financial, and scientific applications of all kinds.Learning Methods: Supervised and unsupervised learning methods (including learning decision and regression trees, rules, connectionist networks, probabilistic networks and other statistical models, inductive logic programming, case-based methods, ensemble methods, clustering, etc.); Reinforcement learning; Evolution-based methods; Explanation-based learning; Analogical learning methods; Automated knowledge acquisition; Learning from instruction; Visualization of patterns in data; Learning in integrated architectures; Multistrategy learning; Multi-agent learning. Papers describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems (e.g., inherent complexity) or methods (e.g., relative performance of alternative algorithms) provide solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications…

Special Issues on this journal

Closed Special Issues