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Published by Packt Publishing 9/27/2022, 2022
ISBN 10: 1803236426 ISBN 13: 9781803236421
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Paperback or Softback. Condition: New. Bioinformatics with Python Cookbook - Third Edition: Use modern Python libraries and applications to solve real-world computational biology problems. Book.
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Language: English
Published by Packt Publishing 2022-09-27, 2022
ISBN 10: 1803236426 ISBN 13: 9781803236421
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Language: English
Published by Berlin, Springer Berlin Heidelberg, 2005
ISBN 10: 3540250069 ISBN 13: 9783540250067
Seller: Antiquariat Bookfarm, Löbnitz, Germany
Hardcover. 295 S. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. Ex-library with stamp and library-signature. GOOD condition, some traces of use. 9783540250067 Sprache: Englisch Gewicht in Gramm: 550.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on themodelling and optimization aspects of the feature selection problem throughcomputational intelligence methods in complex, high-dimensional supervisedmachine learning. To aidreaders in conductingresearch in this field, it covers fundamental concepts and state-of-the-artalgorithms. This book also providesa detailed insight into applying these algorithms into real-world applications.The authors begin by introducing thedefinition high-dimensional machine learning (ML) problems and the challenges they pose. Subsequently, they delve intodimension reduction methods for high-dimensional ML, including global and localfeature selection (FS) techniques. This book also comprehensively presents computationalintelligence methods such as evolutionary computation and deep neural networksfor FS, supported by boththeoretical and empirical evidence. Furthermore, this book explores real-worldscenario applications involving high-dimensional ML, particularly in the context of smartcities, bioinformatics and industrial informatics.This book is a suitable readfor postgraduates and researchers who are interested in the research areas ofcomputational intelligence, soft computing, machine learning and deep learning.Professionals and practitioners within these related fields will also benefitfrom this book.
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Taschenbuch. Condition: Neu. Computational Intelligence for High-Dimensional Machine Learning | A Feature Selection Perspective and Its Real-World Applications | Yu Zhou (u. a.) | Taschenbuch | ix | Englisch | 2025 | Springer | EAN 9789819626861 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Add to basketPaperback. Condition: Brand New. 2011 edition. 184 pages. 9.10x6.00x0.50 inches. In Stock.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Mathematical finance and computing are parts of nearly all aspects of everyday life. The discipline has underpinned beneficial modern capabilities, financial modelling, predictive analysis and optimization including search algorithms, financial data visualisation, financial computing, numerical analysis for predictions, and quantitative risk management. Mathematics and computer science are constantly evolving and contributing to most areas of science, engineering and finance; therefore, future generations of mathematical scientists should reassess the increasingly cross-disciplinary nature of the mathematical sciences. Mathematical Finance and Financial Computing with Real-World Applications presents current scientific and technological innovations from leading academics, researchers, and experts across the globe in mathematical sciences and computing. The volume will discuss new technical ideas and features that can be incorporated into day-to-day life for the benefit of society. A diversified spectrum of scientific advancements is discussed, including applications of stochastics differential equation, numerical analysis, financial computation, computation dynamics, nonlinear optimization, machine learning, and artificial intelligence. Readers will explore diverse ideas and innovations in the field of financial computation and its growing connections to various fields of mathematics and statistics. This book will benefit young and middle level researchers in quantitative finance, financial computing and quantitative risk management. Professors, students, and researchers working in research areas such as mathematical finance, financial modelling, asset pricing, volatility modelling, risk analysis, predictive modelling, portfolio optimization, statistical finance, financial computing, financial engineering, financial data analysis and data science can use this book to improve their results.
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