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Health care rationing --- gezondheidseconomie (gezondheidszorgeconomie) --- économie de la santé (économie des soins de santé) --- Health resources rationing --- Medical care rationing --- Rationing of health care --- Rationing of medical care --- Medical economics --- Medical policy --- Rationing

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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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizatio

Information Technology --- General and Others --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Machine learning --- Learning, Machine --- Artificial intelligence --- Machine theory --- Inteligencia del negocio (40154309) --- Bibliografía recomendada

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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.

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Data mining. --- Data mining --- 301 --- AA / International- internationaal --- 681.3*H28 --- 681.3*H2 --- 681.3*H3 --- 681.3*I26 --- 681.3*J3 --- Techniek van statistische inlichtingen. Organisatie van de statistische enquêtes. Statistische kritiek --- Information storage and retrieval --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- 681.3*J3 Life and medical sciences (Computer applications) --- Life and medical sciences (Computer applications) --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*H2 Database management: security; integrity; protection--See also {?681.5*E5} --- Database management: security; integrity; protection--See also {?681.5*E5} --- 681.3*H28 Database applications --- Database applications

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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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics