Garzon Max (80 results)

Dimensionality Reduction in Data Science
Garzon, Max (EDT); Yang, Ching-chi (EDT); Venugopal, Deepak (EDT); Kumar, Nirman (EDT); Jana, Kalidas (EDT)
- Hardcover
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- Hardcover
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- Hardcover
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Dimensionality Reduction in Data Science
Garzon, Max (EDT); Yang, Ching-chi (EDT); Venugopal, Deepak (EDT); Kumar, Nirman (EDT); Jana, Kalidas (EDT)
- Hardcover
Seller: GreatBookPrices, Columbia, U.S.A.GreatBookPrices
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Condition: As New. Unread book in perfect condition.

Language: English
Published by Springer 1995
Series: Texts in Theoretical Computer Science. An EATCS, Book 3 of 45. Book 3 of 45 - Texts in Theoretical Computer Science. An EATCS
- Hardcover
Seller: Wissenschaftl. Antiquariat Th. Haker e.K, Klettgau, GermanyWissenschaftl. Antiquariat Th. Haker e.K
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hardcover. Condition: Sehr gut. 286 p. In very good condition. ISBN: 9783540561491 Sprache: Englisch Gewicht in Gramm: 549.

Language: English
Published by Springer International Publishing AG, Cham 2022
- Hardcover
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Hardcover. Condition: new. Hardcover. This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and m…athematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction,statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
More imagesLanguage: English
Published by Springer International Publishing AG, CH 2022
- Hardcover
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Hardback. Condition: New. 2022 ed. This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and math…ematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction,statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.

- Hardcover
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Published by Springer 2012
- Softcover
Seller: Books From California, Simi Valley, U.S.A.Books From California
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paperback. Condition: Good. Ex-library copy with stamps and stickers. The pages are lightly tanned. The copy shows minor external wear, but is in otherwise clean condition.

- Softcover
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Language: English
Published by Springer 2012
Series: Texts in Theoretical Computer Science. An EATCS, Book 3 of 45. Book 3 of 45 - Texts in Theoretical Computer Science. An EATCS
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Language: English
Published by Springer 2012-02 2012
Series: Texts in Theoretical Computer Science. An EATCS, Book 3 of 45. Book 3 of 45 - Texts in Theoretical Computer Science. An EATCS
- Softcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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- Softcover
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Dimensionality Reduction in Data Science
Garzon, Max (EDT); Yang, Ching-chi (EDT); Venugopal, Deepak (EDT); Kumar, Nirman (EDT); Jana, Kalidas (EDT)
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Dimensionality Reduction in Data Science
Max Garzon, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana, Lih-Yuan Deng
- Hardcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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- Hardcover
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Language: English
Published by Springer 2012
Series: Texts in Theoretical Computer Science. An EATCS, Book 3 of 45. Book 3 of 45 - Texts in Theoretical Computer Science. An EATCS
- Softcover
Seller: Books Puddle, New York, U.S.A.Books Puddle
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Condition: New. pp. 292.

Dimensionality Reduction in Data Science
Garzon, Max (EDT); Yang, Ching-chi (EDT); Venugopal, Deepak (EDT); Kumar, Nirman (EDT); Jana, Kalidas (EDT)
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Condition: As New. Unread book in perfect condition.

- Softcover
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Language: English
Published by Springer-Verlag 2012
Series: Texts in Theoretical Computer Science. An EATCS, Book 3 of 45. Book 3 of 45 - Texts in Theoretical Computer Science. An EATCS
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. reprint edition. 286 pages. 9.25x6.10x0.66 inches. In Stock.

- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 1st edition. 292 pages. 9.25x6.25x0.75 inches. In Stock.

- Softcover
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Condition: New. pp. 280.

Language: English
Published by CRC Press 2019-02-07 2019
Series: CRC Press Revivals, Book 31 of 151. Book 31 of 151 - CRC Press Revivals
- Softcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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Language: English
Published by Springer Berlin Heidelberg 2012
Series: Texts in Theoretical Computer Science. An EATCS, Book 3 of 45. Book 3 of 45 - Texts in Theoretical Computer Science. An EATCS
- Softcover
Seller: moluna, Greven, Germanymoluna
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- Hardcover
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- Hardcover
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Dimensionality Reduction in Data Science
Garzon, Max (Editor) / Yang, Ching-Chi (Editor) / Venugopal, Deepak (Editor) / Kumar, Nirman (Editor) / Jana, Kalidas (Editor) / Deng, Lih-Yuan (Editor)
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 276 pages. 9.25x6.10x0.67 inches. In Stock.

Language: English
Published by Springer, Berlin|Springer International Publishing|Springer 2023
- Softcover
Seller: moluna, Greven, Germanymoluna
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- Hardcover
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Hardcover. Condition: Brand New. 276 pages. 9.25x6.10x0.69 inches. In Stock.

- Softcover
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a practical and fairly comprehensive review of Data Science through the lensof dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and soluti…ons from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction,statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages toother solutions.