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Latent Factor Analysis for High-Dimensional and Sparse Matrices: A Particle Swarm Optimization-Based Approach (en Inglés)
Ye Yuan
(Autor)
·
Xin Luo
(Autor)
·
Springer
· Tapa Blanda
Latent Factor Analysis for High-Dimensional and Sparse Matrices: A Particle Swarm Optimization-Based Approach (en Inglés) - Yuan, Ye ; Luo, Xin
S/ 248,90
S/ 497,80
Ahorras: S/ 248,90
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Reseña del libro "Latent Factor Analysis for High-Dimensional and Sparse Matrices: A Particle Swarm Optimization-Based Approach (en Inglés)"
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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