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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization : Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Language: English
Published by Taylor & Francis Ltd, 2023
ISBN 10: 103204103X ISBN 13: 9781032041032
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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization : Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
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Add to basketPaperback. Condition: Brand New. 174 pages. 9.19x6.13x0.40 inches. In Stock.
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Taschenbuch. Condition: Neu. Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization | B. K. Tripathy (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | CRC Press | EAN 9781032041032 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
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Add to basketGebunden. Condition: New. Dr. B. K. Tripathy, a distinguished researcher in Mathematics and Computer Science has more than 600 publications to his credit in international journals, conference proceedings, chapters in edited research volumes, edited volumes, monog.
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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Language: English
Published by Taylor & Francis Ltd Sep 2021, 2021
ISBN 10: 1032041013 ISBN 13: 9781032041018
Buch. Condition: Neu. Neuware - 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.
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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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.FEATURESDemonstrates how unsupervised learning approaches can be used for dimensionality reductionNeatly explains algorithms with a focus on the fundamentals and underlying mathematical conceptsDescribes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for useProvides use cases, illustrative examples, and visualizations of each algorithmHelps 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. 176 pp. Englisch.
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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Language: English
Published by Taylor & Francis Ltd, 2023
ISBN 10: 103204103X ISBN 13: 9781032041032
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti GhelaUnsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.FEATURESDemonstrates how unsupervised learning approaches can be used for dimensionality reductionNeatly explains algorithms with a focus on the fundamentals and underlying mathematical conceptsDescribes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for useProvides use cases, illustrative examples, and visualizations of each algorithmHelps 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.
