Rangwala Huzefa (45 results)
Language: English
Published by Springer 2018
Series: SpringerBriefs in Computer Science, Book 277 of 322. Book 277 of 322 - SpringerBriefs in Computer Science
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Language: English
Published by Springer 2018
Series: SpringerBriefs in Computer Science, Book 277 of 322. Book 277 of 322 - SpringerBriefs in Computer Science
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Language: English
Published by Springer 2018
Series: SpringerBriefs in Computer Science, Book 277 of 322. Book 277 of 322 - SpringerBriefs in Computer Science
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Language: English
Published by Springer 2018
Series: SpringerBriefs in Computer Science, Book 277 of 322. Book 277 of 322 - SpringerBriefs in Computer Science
- Softcover
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Language: English
Published by Springer 2018
Series: SpringerBriefs in Computer Science, Book 277 of 322. Book 277 of 322 - SpringerBriefs in Computer Science
- Softcover
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Condition: New. 1st ed. 2018 edition NO-PA16APR2015-KAP.
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Language: English
Published by Springer 2018
Series: SpringerBriefs in Computer Science, Book 277 of 322. Book 277 of 322 - SpringerBriefs in Computer Science
- Softcover
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Language: English
Published by Springer-Verlag New York Inc 2018
Series: SpringerBriefs in Computer Science, Book 277 of 322. Book 277 of 322 - SpringerBriefs in Computer Science
- Softcover
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Paperback. Condition: Brand New. 76 pages. 9.00x6.00x0.50 inches. In Stock.
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Paperback. Condition: Brand New. 199 pages. 9.00x6.00x0.75 inches. In Stock.
Language: English
Published by Springer International Publishing, Springer International Publishing 2018
Series: SpringerBriefs in Computer Science, Book 277 of 322. Book 277 of 322 - SpringerBriefs in Computer Science
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this book, the autho…rs provide a comprehensive overview of various state-of-the-art existing methods and algorithms that were developed to solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in detail such as: 1. High imbalance between classes at different levels of the hierarchy2. Incorporating relationships during model learning leads to optimization issues 3. Feature selection 4. Scalability due to large number of examples, features and classes 5. Hierarchical inconsistencies 6. Error propagation due to multiple decisions involved in making predictions for top-down methodsThe brief also demonstrates how multiple hierarchies can be leveraged forimproving the HC performance using different Multi-Task Learning (MTL) frameworks.The purpose of this book is two-fold:1. Help novice researchers/beginners to get up to speed by providing a comprehensive overview of several existing techniques. 2. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC.New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature.
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Condition: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
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Condition: New. This book helps unravel the relationship of pure sequence information and three-dimensional structure, which remains one of the great fundamental problems in molecular biology and bioinformatics. Editor(s): Rangwala, Huzefa; Karypis, George. Series: Wiley Series in Bioinformatics. Num Pages: 500 pages, Illustrati…ons. BIC Classification: PSBC. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 239 x 164 x 34. Weight in Grams: 952. . 2010. 1st Edition. hardcover. . . . .
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Seller: moluna, Greven, , Germanymoluna
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Gebunden. Condition: New. DR. HUZEFA RANGWALA is an assistant professor in computer science and bioengineering at George Mason University. He has published in various conferences and journals on the topic of bioinformatics.DR. GEORGE KARYPIS is a professor in computer science and en.
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Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Learning Analytics in Higher Education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course devel…opment, teaching pedagogy, and student assessment.
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Condition: New. This book helps unravel the relationship of pure sequence information and three-dimensional structure, which remains one of the great fundamental problems in molecular biology and bioinformatics. Editor(s): Rangwala, Huzefa; Karypis, George. Series: Wiley Series in Bioinformatics. Num Pages: 500 pages, Illustrati…ons. BIC Classification: PSBC. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 239 x 164 x 34. Weight in Grams: 952. . 2010. 1st Edition. hardcover. . . . . Books ship from the US and Ireland.
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Hardcover. Condition: Brand New. 1st edition. 516 pages. 9.37x6.38x1.34 inches. In Stock.
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Buch. Condition: Neu. Neuware - A look at the methods and algorithms used to predict protein structureA thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To tha…t end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology.With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered:\* Databases and resources that are commonly used for protein structure prediction\* The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI)\* Definitions of recurring substructures and the computational approaches used for solving sequence problems\* Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems\* Structure prediction methods that rely on homology modeling, threading, and fragment assembly\* Hybrid methods that achieve high-resolution protein structures\* Parts of the protein structure that may be conserved and used to interact with other biomolecules\* How the loop prediction problem can be used for refinement of the modeled structures\* The computational model that detects the differences between protein structure and its modeled mutantWhether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
