Feature Selection Ensemble Methods by Okun Oleg (13 results)
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hardcover. Condition: Sehr gut. 460 Seiten; 9781609605575.2 Gewicht in Gramm: 2.
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Condition: gut. 2011. Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations In englischer Sprache. pages.
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a unique perspective on machine learning aspects of microarray gene expression based cancer classification. This multidisciplinary text is at the intersection of computer science and biology and, as a… result, can be used as a reference book by resear.
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Buch. Condition: Neu. Feature Selection and Ensemble Methods for Bioinformatics | Algorithmic Classification and Implementations | Oleg Okun | Buch | Gebunden | Englisch | 2011 | Medical Information Science Reference | EAN 9781609605575 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]l…ibri[dot]de | Anbieter: preigu Print on Demand.
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine learning is the branch of artificial intelligence whose goal is to develop algorithms that add learning capabilities to computers. Ensembles are an integral part of machine learning. A typical ensemble includes several algorithms perfor…ming the task of prediction of the class label or the degree of class membership for a given input presented as a set of measurable characteristics, often called features. Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations offers a unique perspective on machine learning aspects of microarray gene expression based cancer classification. This multidisciplinary text is at the intersection of computer science and biology and, as a result, can be used as a reference book by researchers and students from both fields. Each chapter describes the process of algorithm design from beginning to end and aims to inform readers of best practices for use in their own research.
