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PC magazine. DOS power tools : techniques, tricks and utilities
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ISBN: 0553345265 Year: 1988 Publisher: New York (N.Y.): Bantam books

Informatique documentaire
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ISBN: 2225841845 9782225841842 Year: 1993 Publisher: Paris Masson


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Financial accounting estimates through statistical sampling by computer
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ISBN: 0471015679 9780471015673 Year: 1976 Publisher: New York (N.Y.): Wiley

Enterprise resource planning systems : systems, life cycle, electronic commerce, and risk
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ISBN: 0521791529 9780521791526 9780511805936 1107111390 1139146769 0511067216 0511557213 0511805934 0511069340 9780511067211 9780511069345 9781107111394 9781139146760 9780511557217 Year: 2000 Publisher: Cambridge : Cambridge University Press,

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Enterprise Resource Planning Systems can provide the foundation for a wide range of e-commerce based processes including web-based ordering and order tracing, inventory management, and built-to-order goods. This book examines the pros and cons of ERP systems, explains how they work, and highlights their role at the heart of e-commerce. The author begins by explaining the background to ERP systems and goes on to discuss specific systems and their capabilities. He then focuses on the ERP life cycle, from initial implementation through to the time when the system goes live. After covering the use of ERP in e-commerce, he concludes by discussing the risks associated with the adoption of ERP systems. The book contains several detailed case-studies and will be an invaluable guide to managers and consultants working with ERP systems. It will also be a useful reference for MBA students taking courses in information systems management.


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Data mining
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ISBN: 9780123748560 0123748569 9780080890364 0080890369 Year: 2011 Publisher: Burlington, MA Morgan Kaufmann

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Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; New chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material. * Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques * Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods * Performance improvement techniques that work by transforming the input or output * Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive Interface.

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