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Now Publishers Inc, 2021. Paperback. New. 188 pages. 9.21x6.14x0.40 inches.
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Minimum-Distortion Embedding Paperback - 2021
by Akshay Agrawal; Alnur Ali; Stephen Boyd
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- Title Minimum-Distortion Embedding
- Author Akshay Agrawal; Alnur Ali; Stephen Boyd
- Binding Paperback
- Pages 188
- Volumes 1
- Language ENG
- Publisher Now Publishers
- Date 2021-09-08
- ISBN 9781680838886 / 1680838881
- Weight 0.6 lbs (0.27 kg)
- Dimensions 9.21 x 6.14 x 0.4 in (23.39 x 15.60 x 1.02 cm)
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Minimum-Distortion Embedding (Foundations and Trends® in Machine Learning)
by Agrawal, Akshay/ Ali, Alnur/ Boyd, Stephen
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- ISBN 10 / ISBN 13
- 9781680838886 / 1680838881
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Minimum-Distortion Embedding
by Akshay Agrawal
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- 9781680838886 / 1680838881
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New. Embeddings provide concrete numerical representations of otherwise abstract items, for use in downstream tasks. For example, a biologist might look for subfamilies of related cells by clustering embedding vectors associated with individual cells, while a machine learning practitioner might use vector representations of words as features for a classification task. In this monograph the authors present a general framework for faithful embedding called minimum-distortion embedding (MDE) that generalizes the common cases in which similarities between items are described by weights or distances. The MDE framework is simple but general. It includes a wide variety of specific embedding methods, including spectral embedding, principal component analysis, multidimensional scaling, Euclidean distance problems, etc.The authors provide a detailed description of minimum-distortion embedding problem and describe the theory behind creating solutions to all aspects. They also give describe in detail algorithms…
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Minimum-Distortion Embedding (Foundations and Trends(r) in Machine Learning)
by Agrawal, Akshay
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- 9781680838886 / 1680838881
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paperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
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$163.46
FREE shipping to USA
Minimum-Distortion Embedding (Foundations and Trends(r) in Machine Learning)
by Agrawal, Akshay; Ali, Alnur; Boyd, Stephen
- New
- Paperback
- Condition
- New
- Binding
- Paperback
- ISBN 10 / ISBN 13
- 9781680838886 / 1680838881
- Quantity Available
- 2
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Kraków, Poland
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$63.15$16.42 shipping to USA
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Now Publishers, 2021 8vo (23.5 cm). X, 174 pp. Laminated wrappers. "Embeddings provide concrete numerical representations of otherwise abstract items, for use in downstream tasks. For example, a biologist might look for subfamilies of related cells by clustering embedding vectors associated with individual cells, while a machine learning practitioner might use vector representations of words as features for a classification task. In this monograph the authors present a general framework for faithful embedding called minimum-distortion embedding (MDE) that generalizes the common cases in which similarities between items are described by weights or distances. The MDE framework is simple but general. It includes a wide variety of specific embedding methods, including spectral embedding, principal component analysis, multidimensional scaling, Euclidean distance problems, etc. The authors provide a detailed description of minimum-distortion embedding problem and describe the theory behind creating…
Read More Item Price
$63.15
$16.42
shipping to USA