Téléchargez le livre : Representation in Machine Learning

Representation in Machine Learning

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Éditeur :

Springer


Collection :

SpringerBriefs in Computer Science

Paru le : 2023-01-20

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Description


This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book. In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques’ effectiveness.
Pages
93 pages
Collection
SpringerBriefs in Computer Science
Parution
2023-01-20
Marque
Springer
EAN papier
9789811979071
EAN PDF
9789811979088
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
9
Taille du fichier
4209 Ko
Prix
52,74 €
EAN EPUB
9789811979088
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
9
Taille du fichier
24320 Ko
Prix
52,74 €

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