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Record Nr. |
UNINA9910141493103321 |
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Autore |
Albalate Amparo |
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Titolo |
Semi-supervised and unsupervised machine learning [[electronic resource] ] : novel strategies / / Amparo Albalate, Wolfgang Minker |
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Pubbl/distr/stampa |
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London, : ISTE |
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Hoboken, N.J., : Wiley, 2011 |
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ISBN |
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1-118-55769-7 |
1-299-13991-4 |
1-118-58633-6 |
1-118-58613-1 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (256 p.) |
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Collana |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Data mining |
Discourse analysis - Statistical methods |
Speech processing systems |
Computational intelligence |
Electronic books. |
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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 and index. |
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Nota di contenuto |
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pt. 1. State of the art -- pt. 2. Approaches to semi-supervised classification -- pt. 3. Contributions to unsupervised classification, algorithms to detect the optimal number of clusters. |
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Sommario/riassunto |
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This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic mic |
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