9783659926204 - knowledge discovery using machine learning algorithms de dash, sujata; patra, bichitrananda (6 resultados)

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    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2016

      3659926205 / 9783659926204

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      Librería: preigu, Osnabrück, Alemaniapreigu

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      Taschenbuch. Condición: Neu. Knowledge Discovery using Machine Learning Algorithms | Sujata Dash (u. a.) | Taschenbuch | 192 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659926204 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2016

      3659926205 / 9783659926204

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      Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books

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      Paperback. Condición: Brand New. 192 pages. 8.66x5.91x0.44 inches. In Stock.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing Okt 2016, 2016

      3659926205 / 9783659926204

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      Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

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      Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks due to their learning abilities to construct hypotheses that can explain complex relationships in the data. This research explores the effectiveness of integrated and hybrid feature extraction methods proposed in the following chapters, in analyzing gene expression activities, based on a specific tumor disease and identifying the informative genes that underlie different precision levels in the extraction process. The identified gene subset may give an enhanced insight on the gene-gene interaction in response to different stages of abnormal cell growth which could be vital in designing treatment strategies to prevent any progression of abnormal cells. 192 pp. Englisch.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2016

      3659926205 / 9783659926204

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      Librería: moluna, Greven, Alemaniamoluna

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      EUR 52,90

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      Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Dash SujataDr. Sujata Dash is currently working as an Associate Professor of Computer Science Department of North Orissa University, Baripada, Odisha, India. She has 25 years of teaching and 17 years of research experience. She has p.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing Okt 2016, 2016

      3659926205 / 9783659926204

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      Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000

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      Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks due to their learning abilities to construct hypotheses that can explain complex relationships in the data. This research explores the effectiveness of integrated and hybrid feature extraction methods proposed in the following chapters, in analyzing gene expression activities, based on a specific tumor disease and identifying the informative genes that underlie different precision levels in the extraction process. The identified gene subset may give an enhanced insight on the gene-gene interaction in response to different stages of abnormal cell growth which could be vital in designing treatment strategies to prevent any progression of abnormal cells.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 192 pp. Englisch.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2016

      3659926205 / 9783659926204

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      Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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      EUR 64,90

      Envío por EUR 61,52 
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      Cantidad disponible: 1 disponibles

      Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks due to their learning abilities to construct hypotheses that can explain complex relationships in the data. This research explores the effectiveness of integrated and hybrid feature extraction methods proposed in the following chapters, in analyzing gene expression activities, based on a specific tumor disease and identifying the informative genes that underlie different precision levels in the extraction process. The identified gene subset may give an enhanced insight on the gene-gene interaction in response to different stages of abnormal cell growth which could be vital in designing treatment strategies to prevent any progression of abnormal cells.