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Record Nr. |
UNINA9910463028503321 |
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Autore |
Amorim Silvia |
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Titolo |
José Saramago : art, théorie et éthique du roman / / Silvia Amorim |
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Pubbl/distr/stampa |
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Paris : , : L'Harmattan, , [2010] |
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©2010 |
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ISBN |
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Descrizione fisica |
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1 online resource (293 p. ) |
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Collana |
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Disciplina |
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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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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references (pages 279-290). |
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Sommario/riassunto |
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L'écrivain portuguais José Saramago est un grand nom de la littérature mondiale, récompensé par le prix Nobel de littérature en 1998. En tant que citoyen, il est également connu pour son scepticisme à l'égard de la société occidentale actuelle et pour son engagement politique. Ses prises de position énergiques transparaissent dans ses romans. Mais, quels sont les liens entre le roman saramaguien et la société ? J. Saramago propose une réflexion profonde sur l'homme, l'histoire, les fondements de notre identité et de notre morale. Son écriture est marquée par la transgression et l'ironie. |
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2. |
Record Nr. |
UNINA9910437873403321 |
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Autore |
Xanthopoulos Petros |
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Titolo |
Robust data mining / / Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis |
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Pubbl/distr/stampa |
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New York, : Springer, 2013 |
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ISBN |
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1-283-90917-0 |
1-4419-9878-0 |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (66 p.) |
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Collana |
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SpringerBriefs in optimization, , 2190-8354 |
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Altri autori (Persone) |
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PardalosP. M <1954-> (Panos M.) |
TrafalisTheodore B |
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Disciplina |
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Soggetti |
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Data mining |
Robust optimization |
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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. |
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Nota di contenuto |
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1. Introduction -- 2. Least Squares Problems -- 3. Principal Component Analysis -- 4. Linear Discriminant Analysis -- 5. Support Vector Machines -- 6. Conclusion. |
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Sommario/riassunto |
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Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field. |
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