Machine Learning and Knowledge Extraction

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