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1. |
Record Nr. |
UNISALENTO991001591769707536 |
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Autore |
Grigoryan, Alexander |
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Titolo |
Heat kernel and analysis on manifolds / Alexander Grigor'yan |
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Pubbl/distr/stampa |
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Providence, R. I. : American Mathematical Society |
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[Somerville, Mass.] : International Press, c2009 |
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ISBN |
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Descrizione fisica |
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xvii, 482 p. : ill. ; 27 cm |
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Collana |
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AMS/IP studies in advanced mathematics, 1089-3288 ; 47 |
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Classificazione |
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AMS 58J35 |
AMS 31B05 |
AMS 35K05 |
AMS 35P15 |
AMS 47D07 |
AMS 53C20 |
LC QA377.G754 |
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Disciplina |
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Soggetti |
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Heat equation |
Kernel functions |
Riemannian manifolds |
Gaussian processes |
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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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Nota di bibliografia |
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Includes bibliographical references and index |
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2. |
Record Nr. |
UNINA9910299900703321 |
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Autore |
Melin Patricia |
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Titolo |
New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension / / by Patricia Melin, German Prado-Arechiga |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (88 pages) : illustrations, tables |
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Collana |
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SpringerBriefs in Computational Intelligence, , 2625-3712 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Biomedical engineering |
Medical informatics |
Computational Intelligence |
Biomedical Engineering and Bioengineering |
Health Informatics |
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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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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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From the Content: Introduction -- Fuzzy Logic for Arterial Hypertension Classification -- Design of a Neuro Design of a Neuro Design of Arterial Hypertension. |
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Sommario/riassunto |
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In this book, a new approach for diagnosis and risk evaluation of ar-terial hypertension is introduced. The new approach was implement-ed as a hybrid intelligent system combining modular neural net-works and fuzzy systems. The different responses of the hybrid system are combined using fuzzy logic. Finally, two genetic algo-rithms are used to perform the optimization of the modular neural networks parameters and fuzzy inference system parameters. The experimental results obtained using the proposed method on real pa-tient data show that when the optimization is used, the results can be better than without optimization. This book is intended to be a refer-ence for scientists and physicians interested in applying soft compu-ting techniques, such as neural networks, fuzzy logic and genetic algorithms, in medical diagnosis, but also in general to classification |
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and pattern recognition and similar problems. |
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