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[null,"1.","Record Nr.",null,"UNISALENTO991003274829707536"]
[null,null,"Autore",null,"Capuana, Luigi"]
[null,null,"Titolo",null,"Cardello : racconto / Luiigi Capuana ; illustrato da G.G. Bruno"]
[null,null,"Pubbl/distr/stampa",null,"Milano : Sandron, [19..]"]
[null,null,"Descrizione fisica",null,"248 p. ; 20 cm"]
[null,null,"Disciplina",null,"853.8"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910483851403321"]
[null,null,"Titolo",null,"Deep Learners and Deep Learner Descriptors for Medical Applications / / edited by Loris Nanni, Sheryl Brahnam, Rick Brattin, Stefano Ghidoni, Lakhmi C. Jain"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020"]
[null,null,"ISBN",null,"3-030-42750-1"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (286 pages)"]
[null,null,"Collana",null,"Intelligent Systems Reference Library, , 1868-4394 ; ; 186"]
[null,null,"Disciplina",null,"610.28"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Medical informatics"]
[{"$nulls":4},"Biomedical engineering"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Health Informatics"]
[{"$nulls":4},"Biomedical Engineering and Bioengineering"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,null,"Sommario/riassunto","This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those "]
[{"$nulls":4},"interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects. ."]