About Machine Learning and Knowledge Extraction
Aims
Machine Learning and Knowledge Extraction (ISSN 2504-4990) provides an advanced forum for studies related to all areas of machine learning and knowledge extraction. It publishes reviews, regular research papers, communications, perspectives, and viewpoints, as well as Special Issues on particular subjects.
The aim of Machine Learning and Knowledge Extraction is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. Therefore, the journal has no restrictions regarding the length of papers. Full experimental details should be provided so that the results can be reproduced.
Scope
- Machine learning
- Knowledge representation
- Artificial intelligence
- Knowledge extraction
- Neural network
- Natural language processing
- Unsupervised learning
- Privacy
- Uncertainty
- Transfer learning
- Image classification
- Information retrieval
- Feature selection
- Visualization
- Network- and graph-based machine learning
- Geometric machine learning and topology
- Entropy and machine learning applications
MDPI Publication Ethics Statement
MDPI is a member of the Committee on Publication Ethics (COPE).
MDPI takes the responsibility to enforce a rigorous peer-review together with strict ethical policies and standards to ensure to add
high quality scientific works to the field of scholarly publication. Unfortunately, cases of plagiarism, data falsification, inappropriate
authorship credit, and the like, do arise. MDPI takes such publishing ethics issues very seriously and our editors are trained to proceed in
such cases with a zero tolerance policy. To verify the originality of content submitted to our journals, we use iThenticate to check submissions against previous publications.
Book Reviews
Authors and publishers are encouraged to send review copies of their recent related books to the following address. Received books will be listed as Books Received within the journal's News & Announcements section.
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Copyright / Open Access
Articles published in MAKE will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution License (CC BY). The copyright is retained by the author(s). MDPI will insert the following note at the end of the published text:
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