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Latent Factor Analysis for High-dimensional and Sparse Matrices

- A particle swarm optimization-based approach
Af: Ye Yuan, Xin Luo Engelsk Paperback

Latent Factor Analysis for High-dimensional and Sparse Matrices

- A particle swarm optimization-based approach
Af: Ye Yuan, Xin Luo Engelsk Paperback
Tjek vores konkurrenters priser
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.

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Tjek vores konkurrenters priser
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.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 92
ISBN-13: 9789811967023
Indbinding: Paperback
Udgave:
ISBN-10: 9811967024
Kategori: Machine learning
Udg. Dato: 16 nov 2022
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 16 nov 2022
Forfatter(e): Ye Yuan, Xin Luo
Forfatter(e) Ye Yuan, Xin Luo

Kategori Machine learning

ISBN-13 9789811967023

Sprog Engelsk

Indbinding Paperback

Sider 92

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 16 nov 2022

Oplagsdato 16 nov 2022

Forlag Springer Verlag, Singapore

Kategori sammenhænge