Téléchargez le livre : Latent Factor Analysis for High-dimensional and Sparse Matrices

Latent Factor Analysis for High-dimensional and Sparse Matrices

A particle swarm optimization-based approach de

,

Éditeur :

Springer


Collection :

SpringerBriefs in Computer Science

Paru le : 2022-11-15

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Description
Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question.
This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications.
The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.
Pages
92 pages
Collection
SpringerBriefs in Computer Science
Parution
2022-11-15
Marque
Springer
EAN papier
9789811967023
EAN PDF
9789811967030
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
9
Taille du fichier
4106 Ko
Prix
47,46 €
EAN EPUB
9789811967030
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
9
Taille du fichier
19180 Ko
Prix
47,46 €

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