...THE CUTTING EDGE IN AI RESEARCH....
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.
The journal features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods 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 problems. Research methodology papers improve how machine learning research is conducted.
All papers describe the supporting evidence in ways that can be verified or replicated by other researchers. The papers also detail the learning component clearly and discuss assumptions regarding knowledge representation and the performance task.
Journal Citation Reports®, Thomson Reuters
Science Citation Index, Science Citation Index Expanded (SciSearch), Journal Citation Reports/Science Edition, SCOPUS, PsycINFO, INSPEC, Zentralblatt Math, Google Scholar, EBSCO Discovery Service, CSA, Academic OneFile, ACM Digital Library, Computer Abstracts International Database, Computer Science Index, CSA Environmental Sciences, Current Contents/Engineering, Computing and Technology, DBLP, Earthquake Engineering Abstracts, EBSCO Discovery Service, EI-Compendex, Gale, io-port.net, Mathematical Reviews, OCLC, OmniFile, PASCAL, PSYCLINE, Referativnyi Zhurnal (VINITI), Science Select, SCImago, Summon by ProQuest