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Publicado por CRC Press 2023-09-25, Boca Raton, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: Blackwell's, London, Reino Unido
Libro
paperback. Condición: New. Language: ENG.
Publicado por Taylor & Francis Ltd, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
Libro
Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Publicado por CRC Press, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
Libro Impresión bajo demanda
PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Publicado por CRC Pr I Llc, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: Revaluation Books, Exeter, Reino Unido
Libro
Paperback. Condición: Brand New. 174 pages. 9.19x6.13x0.40 inches. In Stock.
Publicado por Taylor & Francis Ltd, London, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: Grand Eagle Retail, Wilmington, DE, Estados Unidos de America
Libro
Paperback. Condición: new. Paperback. Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization.FEATURES Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with a focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use Provides use cases, illustrative examples, and visualizations of each algorithm Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysisThis book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction. This book describes algorithms like Locally Linear Embedding, Laplacian eigenmaps, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in case of non-linear relationships within the data. Underlying mathematical concepts, derivations, proofs, strengths and limitations of these algorithms are discussed as well. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por CRC Press, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Libro
Condición: New. In.
Publicado por CRC Press, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: Books Unplugged, Amherst, NY, Estados Unidos de America
Libro
Condición: New. Buy with confidence! Book is in new, never-used condition 0.71.
Publicado por CRC Press, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: California Books, Miami, FL, Estados Unidos de America
Libro
Condición: New.
Publicado por CRC Pr I Llc, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: Revaluation Books, Exeter, Reino Unido
Libro
Paperback. Condición: Brand New. 174 pages. 9.19x6.13x0.40 inches. In Stock.
Publicado por Taylor & Francis Ltd, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
Libro Impresión bajo demanda
Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Publicado por CRC Press, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: moluna, Greven, Alemania
Libro Impresión bajo demanda
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti GhelaDemonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts .
Publicado por CRC Press, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
Libro Impresión bajo demanda
PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Publicado por CRC Press, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: Mispah books, Redhill, SURRE, Reino Unido
Libro
paperback. Condición: New. New. book.
Publicado por Taylor & Francis Ltd, London, 2023
ISBN 10: 103204103XISBN 13: 9781032041032
Librería: AussieBookSeller, Truganina, VIC, Australia
Libro
Paperback. Condición: new. Paperback. Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization.FEATURES Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with a focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use Provides use cases, illustrative examples, and visualizations of each algorithm Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysisThis book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction. This book describes algorithms like Locally Linear Embedding, Laplacian eigenmaps, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in case of non-linear relationships within the data. Underlying mathematical concepts, derivations, proofs, strengths and limitations of these algorithms are discussed as well. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.