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Machine Learning and Knowledge Extraction an Open Access Journal by MDPI machine learning and knowledge extraction an Open Access Journal by MDPI Academic Open Access Publishing since 1996 machine learning and knowledge extraction an Open Access Journal by MDPI Editor-in-Chief Message from the Editor-in-Chief Prof. Dr. Andreas Holzinger Machine learning deals with understanding intelligence to design algorithms that can learn from data, gain knowledge from experience and improve their learning behaviour over time. The challenge is to extract relevant structural and/or temporal patterns (“knowledge”) from data, which is often hidden in high dimensional spaces, thus not accessible to humans. Many application domains, e.g., smart health, smart factory, etc. affect our daily life, e.g., recommender systems, speech recognition, autonomous driving, etc. The grand challenge is to understand the context in the real-world under uncertainty. Probabilistic inference can be of great help here as the inverse probability allows to learn from data, to infer unknowns, and to make predictions to support decision making. Author Benefits Open Access Unlimited and free access for readers No Copyright Constraints Retain copyright of your work and free use of your article Thorough Peer-Review No Space Constraints, No Extra Space or Color Charges No restriction on the length of the papers, number of figures or colors Aims and Scope Machine Learning and Knowledge Extraction (MAKE) is an inter-disciplinary, cross-domain, peer-reviewed, scholarly open access journal to provide a platform to support the international machine learning community. It publishes original research articles, reviews, tutorials, research ideas, short notes and Special Issues that focus on machine learning and applications. Papers which deal with funda- mental research questions to help reach a level of useable computational intelligence are very welcome. Machine Learning methods, algorithms and tools, and Data preprocessing, -fusion, -integration, -mapping, -generation, -augmentation, open data, Knowledge representation, stochastic ontologies, partial Context and model construction, Federated learning, client-side Learning, Bayesian deep Learning, deep transfer learning, Interactive machine learning with a human-in-the-loop, Empirical studies, comparisons to human cognition, Natural learning and evolutionary approaches, multi-agent Systems and hybrid approaches, Graphical models and network approaches, Graph-based data mining, topological data mining, Entropy-based data mining, Visualization, decision making, decision support, Editorial Office MAKE Editorial Office Data protection, safety, security, privacy aware machine [email protected] learning, MDPI, St. Alban-Anlage 66 4052 Basel, Switzerland Ethical, social, legal and educational aspects, Tel: +41 61 683 77 34 Usability, user and gender studies, acceptance, assessment, Fax: +41 61 302 89 18 Business issues, www.mdpi.com mdpi.com/journal/make Domain specifics, e.g. health, industry, ... MDPI is a member of Follow Us facebook.com/MDPIOpenAccessPublishing twitter.com/MDPIOpenAccess linkedin.com/company/mdpi weibo.com/mdpicn Wechat: MDPI-China blog.mdpi.com MDPI St. Alban-Anlage 66 CH-4052 Basel Switzerland Tel: +41 61 683 77 34 Fax: +41 61 302 89 18 www.mdpi.com mdpi.com/journal/make See www.mdpi.com for a full list of offices and contact information. MDPI is a company registered in Basel, Switzerland, No. CH-270.3.014.334-3, whose registered office is at St. Alban-Anlage 66, CH-4052 Basel, Switzerland. Basel, July 2021.
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