Librería: World of Books (was SecondSale), Montgomery, IL, Estados Unidos de America
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 109,61
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Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 111,95
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Añadir al carritoHardcover. Condición: new. Hardcover. A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Publicado por Taylor & Francis Group, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Librería: Majestic Books, Hounslow, Reino Unido
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
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Añadir al carritoHardback. Condición: New. New copy - Usually dispatched within 4 working days.
Idioma: Inglés
Publicado por Taylor & Francis Group, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Idioma: Inglés
Publicado por Taylor & Francis Group, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoHardcover. Condición: Brand New. 361 pages. 9.25x6.25x1.00 inches. In Stock.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 170,74
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: new. Hardcover. A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 121,84
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Añadir al carritoHardcover. Condición: Brand New. 361 pages. 9.25x6.25x1.00 inches. In Stock. This item is printed on demand.
Librería: moluna, Greven, Alemania
EUR 93,92
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the Nati.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 184,05
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Añadir al carritoBuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book synthesizes those techniques from numerical analysis, algorithms, data structures, and optimization theory mostcommonly employed in statistics and machine learning. We provide concrete applications of these methods by giving complete reference implementations for a large set of the most commonly used statistical estimators. The goal is to provide a self-contained textbook explaining the inner algorithmic workings of statistical estimators.