Mechanizing Hypothesis Formation: Principles and Case Studies

Author:   Jan Rauch ,  Milan Šimůnek ,  David Chudán ,  Petr Máša
Publisher:   Taylor & Francis Ltd
ISBN:  

9780367549824


Pages:   346
Publication Date:   09 October 2024
Format:   Paperback
Availability:   In Print   Availability explained
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Mechanizing Hypothesis Formation: Principles and Case Studies


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Author:   Jan Rauch ,  Milan Šimůnek ,  David Chudán ,  Petr Máša
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
Weight:   0.666kg
ISBN:  

9780367549824


ISBN 10:   0367549824
Pages:   346
Publication Date:   09 October 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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Jan Rauch graduated from the Faculty of Mathematics and Physics of Charles University in Prague. He received his Ph.D. in Mathematical Logic in 1987 from the Institute of Mathematics of the Czechoslovak Academy of Sciences. He is a full professor at the Department of Information and Knowledge Engineering, Prague University of Economics and Business since 2011. Milan Šimůnek is an associate professor (since 2012) at the Faculty of Informatics and Statistics, Prague University of Economics and Business. His research activities include data mining, databases, virtual reality and software projects development. He is the software project leader of the LISp-Miner system since its launch in 1996 and author of its core-modules implementation. David Chudán is an assistant professor of Applied Informatics at the Faculty of Informatics and Statistics, Prague University of Economics and Business. He received his Ph.D. in 2015 in the field of Applied informatics. His research interests include data mining and machine learning on different tools and platforms. Another research area is GUHA association rules and their complementary usage with business intelligence. Petr Máša graduated from the Prague University of Economics and Business and the Faculty of Mathematics and Physics of Charles University in Prague. He received his Ph.D. in 2006. He also works on business projects where he uses data mining, data science, data analytics and he is also business responsible.

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