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Foreword; G. Piatetsky-Shapiro. Preface. Acknowledgements. 1. The Scope and Methods of the Study. 2. Numerical Data Mining Models with Financial Applications. 3. Rule-Based and Hybrid Financial Data Mining. 4. Relational Data Mining (RDM). 5. Financial Applications of Relational Data Mining. 6. Comparison of Performance of RDM and other methods in financial applications. 7. Fuzzy logic approach and its financial applications. References. Subject Index.
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Carlos Cox
reviewed Data Mining In Finance Advances In Relational And Hybrid Methods on November 06, 2015 Data Mining In Finance Advances In Relational And Hybrid Methods
I manage internal communications and intranet portals and this book was hugely helpful for me to be able to effectively converse with my IT partners on defining business requirements. I highly recommend!
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Add Data Mining In Finance Advances In Relational And Hybrid Methods, Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these app, Data Mining In Finance Advances In Relational And Hybrid Methods to the inventory that you are selling on WonderClubX
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Add Data Mining In Finance Advances In Relational And Hybrid Methods, Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these app, Data Mining In Finance Advances In Relational And Hybrid Methods to your collection on WonderClub |
