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Editore: Cambridge University Press, 2021
ISBN 10: 110879338X ISBN 13: 9781108793384
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ISBN 10: 110879338X ISBN 13: 9781108793384
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Unsupervised Machine Learning for Clustering in Political and Social Research. Book.
Editore: Cambridge University Press 8/5/2021, 2021
ISBN 10: 1108986897 ISBN 13: 9781108986892
Lingua: Inglese
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Modern Dimension Reduction. Book.
Editore: Cambridge University Press, 2021
ISBN 10: 1108986897 ISBN 13: 9781108986892
Lingua: Inglese
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ISBN 10: 110879338X ISBN 13: 9781108793384
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering. Offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered, in addition to R code and real data to facilitate interaction with the concepts. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Cambridge University Press, Cambridge, 2021
ISBN 10: 1108986897 ISBN 13: 9781108986892
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github. Dimension reduction offers researchers and scholars the ability to make complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 110879338X ISBN 13: 9781108793384
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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Editore: Cambridge University Press 2020-10-31, 2020
ISBN 10: 110879338X ISBN 13: 9781108793384
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Da: Chiron Media, Wallingford, Regno Unito
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ISBN 10: 110879338X ISBN 13: 9781108793384
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Editore: Cambridge University Press 2021-07-31, 2021
ISBN 10: 1108986897 ISBN 13: 9781108986892
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Aggiungi al carrelloPaperback. Condizione: Brand New. 75 pages. 8.94x5.91x0.28 inches. In Stock.
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ISBN 10: 110879338X ISBN 13: 9781108793384
Lingua: Inglese
Da: Books Puddle, New York, NY, U.S.A.
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ISBN 10: 110879338X ISBN 13: 9781108793384
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ISBN 10: 110879338X ISBN 13: 9781108793384
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Aggiungi al carrellopaperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Editore: Cambridge University Press, 2021
ISBN 10: 110879338X ISBN 13: 9781108793384
Lingua: Inglese
Da: Speedyhen, London, Regno Unito
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Editore: Cambridge University Press, Cambridge, 2021
ISBN 10: 1108986897 ISBN 13: 9781108986892
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Da: AussieBookSeller, Truganina, VIC, Australia
EUR 38,72
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github. Dimension reduction offers researchers and scholars the ability to make complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Editore: Cambridge University Press, Cambridge, 2021
ISBN 10: 110879338X ISBN 13: 9781108793384
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 38,72
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering. Offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered, in addition to R code and real data to facilitate interaction with the concepts. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.