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1. |
Record Nr. |
UNINA9910705123703321 |
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Autore |
Maurer David C. |
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Titolo |
Department of Homeland Security : DHS's efforts to improve employee morale and fill senior leadership vacancies : testimony before the Committee on Homeland Security, House of Representatives / / statement of David C. Maurer |
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Pubbl/distr/stampa |
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[Washington, D.C.] : , : United States Government Accountability Office, , 2013 |
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Descrizione fisica |
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1 online resource (17 pages) : color illustrations |
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Collana |
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Testimony ; ; GAO-14-228T |
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Soggetti |
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Employee morale - United States |
Government executives - Selection and appointment |
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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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Title from title screen (viewed Feb. 5, 2014). |
"For release ... December 12, 2013." |
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Nota di bibliografia |
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Includes bibliographical references. |
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2. |
Record Nr. |
UNINA9910484925303321 |
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Titolo |
Machine Learning in Medical Imaging : 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings / / edited by Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk |
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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 (XIV, 324 p. 127 illus.) |
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Collana |
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Image Processing, Computer Vision, Pattern Recognition, and Graphics, , 3004-9954 ; ; 10019 |
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Disciplina |
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Soggetti |
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Computer vision |
Pattern recognition systems |
Medical informatics |
Data mining |
Artificial intelligence |
Computer Vision |
Automated Pattern Recognition |
Health Informatics |
Data Mining and Knowledge Discovery |
Artificial Intelligence |
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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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Sommario/riassunto |
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This book constitutes the refereed proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 38 full papers presented in this volume were carefully reviewed and selected from 60 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new |
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cutting-edge techniques and their use in medical imaging. |
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