Publicado por Packt Publishing, Limited, 2022
ISBN 10: 1803242388 ISBN 13: 9781803242385
Idioma: Inglés
Librería: Better World Books, Mishawaka, IN, Estados Unidos de America
EUR 39,62
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Añadir al carritoCondición: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.
Publicado por Packt Publishing, Limited, 2022
ISBN 10: 1803242388 ISBN 13: 9781803242385
Idioma: Inglés
Librería: Better World Books, Mishawaka, IN, Estados Unidos de America
EUR 39,62
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 41,72
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Publicado por Packt Publishing 10/31/2022, 2022
ISBN 10: 1803242388 ISBN 13: 9781803242385
Idioma: Inglés
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de America
EUR 45,65
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Añadir al carritoPaperback or Softback. Condición: New. Machine Learning Techniques for Text: Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluatio 1.68. Book.
Librería: California Books, Miami, FL, Estados Unidos de America
EUR 45,93
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 47,40
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 45,27
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 45,25
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 51,05
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Publicado por LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056843X ISBN 13: 9786200568434
Idioma: Inglés
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 69,51
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Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 70,05
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Publicado por LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056843X ISBN 13: 9786200568434
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
EUR 85,76
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Añadir al carritoPaperback. Condición: Brand New. 152 pages. 8.66x5.91x0.35 inches. In Stock.
Publicado por Chapman and Hall/CRC 2013-12-11, 2013
ISBN 10: 1439857245 ISBN 13: 9781439857243
Idioma: Inglés
Librería: Chiron Media, Wallingford, Reino Unido
EUR 113,10
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Librería: California Books, Miami, FL, Estados Unidos de America
EUR 147,51
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 149,32
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Añadir al carritoCondición: New. pp. 262.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 161,70
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Publicado por Elsevier Science & Technology, San Francisco, 2025
ISBN 10: 0443328188 ISBN 13: 9780443328183
Idioma: Inglés
Librería: Grand Eagle Retail, Mason, OH, Estados Unidos de America
EUR 165,17
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Añadir al carritoPaperback. Condición: new. Paperback. Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
EUR 166,28
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 174,16
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 184,80
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EUR 168,35
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Añadir al carritoPaperback. Condición: Brand New. 250 pages. 9.25x7.50x9.22 inches. In Stock.
EUR 186,65
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Publicado por Elsevier Science & Technology, San Francisco, 2025
ISBN 10: 0443328188 ISBN 13: 9780443328183
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
EUR 165,60
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Añadir al carritoPaperback. Condición: new. Paperback. Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 199,38
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Añadir al carritoPaperback. Condición: Brand New. 196 pages. 9.21x6.14x0.42 inches. In Stock.
Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 223,20
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EUR 235,70
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Añadir al carritoPaperback. Condición: Brand New. 250 pages. 9.25x7.50x9.22 inches. In Stock.
Publicado por Elsevier Science Feb 2025, 2025
ISBN 10: 0443328188 ISBN 13: 9780443328183
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 247,94
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.
Publicado por Continental Academy Press, London
Librería: Continental Academy Press, London, SELEC, Reino Unido
EUR 12,84
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Añadir al carritoSoftcover. Condición: New. Estado de la sobrecubierta: no dj. First. The world of machine learning is a rapidly evolving field, where even the slightest miscalculation can have far-reaching consequences. 'Machine Learning for Clustering and Dimensionality Reduction' offers a comprehensive exploration of the role of machine learning in clustering and dimensionality reduction, examining the various ways in which machine learning algorithms can be used to enhance data analysis, improve model accuracy, and reduce data complexity. From the development of k-means clustering to the use of PCA, this book provides a roadmap for businesses looking to harness the power of machine learning in clustering and dimensionality reduction. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.
Publicado por Continental Academy Press, London
Librería: Continental Academy Press, London, SELEC, Reino Unido
EUR 13,16
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Añadir al carritoSoftcover. Condición: New. Estado de la sobrecubierta: no dj. First. Machine Learning for Dimensionality Reduction and Feature Selection offers a comprehensive exploration of the application of machine learning to dimensionality reduction and feature selection. By examining the latest advances in machine learning and dimensionality reduction algorithms, this book provides a framework for developing the skills and strategies needed to succeed in this field. Through a series of in-depth case studies and practical examples, readers will gain a deep understanding of how to apply machine learning to dimensionality reduction and feature selection, from PCA to recursive feature elimination. By mastering these techniques, machine learning professionals can improve their ability to reduce dimensionality, select relevant features, and improve overall model performance. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.
Publicado por Continental Academy Press, London
Librería: Continental Academy Press, London, SELEC, Reino Unido
EUR 13,52
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Añadir al carritoSoftcover. Condición: New. Estado de la sobrecubierta: no dj. First. In 'Exploring Dimensionality Reduction in Machine Learning', the author presents a comprehensive overview of the mathematical techniques that underlie dimensionality reduction in machine learning. By examining the fundamental principles of linear algebra and statistics, the book demonstrates how these principles can be employed to develop more effective dimensionality reduction strategies. The author illustrates the application of various dimensionality reduction techniques, including principal component analysis, singular value decomposition, and t-distributed stochastic neighbor embedding, in a range of machine learning applications. Through a series of practical examples, the book shows how these techniques can be used to improve model performance and reduce computational complexity. By providing a clear and concise introduction to the role of dimensionality reduction in machine learning, 'Exploring Dimensionality Reduction in Machine Learning' offers a valuable resource for researchers, developers, and students seeking to improve their understanding of machine learning techniques. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.