Verlag: Discovery Publishing House, 2020
ISBN 10: 8171413323 ISBN 13: 9788171413324
Sprache: Englisch
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
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In den WarenkorbZustand: New. pp. [vii] + 324 Figures.
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In den WarenkorbZustand: New. In.
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In den WarenkorbSoftcover. Zustand: Very Good. 1. Auflage. Unread, some shelfwear. Immediately dispatched from Germany.
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In den WarenkorbHardcover. Zustand: Very Good. 1. Auflage. Unread, with some shelfwear. Immediately dispatched from Germany.
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In den WarenkorbZustand: New.
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In den WarenkorbHRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
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In den WarenkorbZustand: New.
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In den WarenkorbHardcover. Zustand: Brand New. 250 pages. 9.18x6.12x9.21 inches. In Stock.
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In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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In den WarenkorbZustand: New. In.
Verlag: Springer International Publishing, Springer International Publishing Sep 2024, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Sprache: Englisch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
EUR 213,99
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In den WarenkorbBuch. Zustand: Neu. Neuware -This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 556 pp. Englisch.
Verlag: Springer International Publishing, Springer International Publishing, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 213,99
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In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 259,15
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In den WarenkorbHardcover. Zustand: Brand New. 321 pages. 9.25x6.25x0.75 inches. In Stock.
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EUR 304,04
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In den WarenkorbHardcover. Zustand: Brand New. 2nd edition. 555 pages. 9.25x6.10x9.21 inches. In Stock.
Verlag: Springer Nature Singapore, Springer Nature Singapore Jun 2023, 2023
ISBN 10: 9811691606 ISBN 13: 9789811691607
Sprache: Englisch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
EUR 267,49
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In den WarenkorbTaschenbuch. Zustand: Neu. Neuware -Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 228 pp. Englisch.
Verlag: Springer Nature Singapore, Springer Nature Singapore Jun 2022, 2022
ISBN 10: 9811691576 ISBN 13: 9789811691577
Sprache: Englisch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
EUR 267,49
Währung umrechnenAnzahl: 2 verfügbar
In den WarenkorbBuch. Zustand: Neu. Neuware -Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 228 pp. Englisch.
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811691576 ISBN 13: 9789811691577
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 270,70
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2023
ISBN 10: 9811691606 ISBN 13: 9789811691607
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 272,37
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 376,87
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In den WarenkorbPaperback. Zustand: Brand New. 228 pages. 9.25x6.10x0.63 inches. In Stock.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 378,85
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In den WarenkorbHardcover. Zustand: Brand New. 228 pages. 9.25x6.10x0.79 inches. In Stock.