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Record Nr. |
UNINA9910741146803321 |
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Autore |
Liu Han |
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Titolo |
Rule Based Systems for Big Data : A Machine Learning Approach / / by Han Liu, Alexander Gegov, Mihaela Cocea |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (127 p.) |
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Collana |
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Studies in Big Data, , 2197-6503 ; ; 13 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Data mining |
Computational Intelligence |
Artificial Intelligence |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Introduction -- Theoretical Preliminaries -- Generation of Classification Rules -- Simplification of Classification Rules -- Representation of Classification Rules -- Ensemble Learning Approaches -- Interpretability Analysis. |
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Sommario/riassunto |
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The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems. |
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