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.
Feature-weighted clustering with inner product induced norm based dissimilarity measures: an optimization perspective
- Journal Title
- Machine Learning
- Volume 1 / 1986 - Volume 106 / 2017
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Industry Sectors
To view the rest of this content please follow the download PDF link above.