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Latent Factor Analysis for High-dimensional and Sparse Matrices
- A particle swarm optimization-based approach
Engelsk Paperback
Latent Factor Analysis for High-dimensional and Sparse Matrices
- A particle swarm optimization-based approach
Engelsk Paperback

388 kr
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Om denne bog
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.

Product detaljer
Sprog:
Engelsk
Sider:
92
ISBN-13:
9789811967023
Indbinding:
Paperback
Udgave:
ISBN-10:
9811967024
Kategori:
Udg. Dato:
16 nov 2022
Længde:
0mm
Bredde:
155mm
Højde:
235mm
Forlag:
Springer Verlag, Singapore
Oplagsdato:
16 nov 2022
Forfatter(e):
Ofte købt sammen
Andet, 2023
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