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Více informací o knize Latent Factor Analysis for High-dimensional and Sparse Matrices

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Anotace knihy

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.

Parametry knihy

Zařazení knihy Knihy v angličtině Computing & information technology Databases

• Plný název: Latent Factor Analysis for High-dimensional and Sparse Matrices
• Podnázev: A particle swarm optimization-based approach
• Autor: Ye Yuan, Xin Luo
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 100
• EAN: 9789811967023
• ID: 41409153
• Nakladatelství: Springer, Berlin
• Hmotnost: 154 g
• Rozměry: 235 × 155 × 6 mm
• Rok vydání: 2022

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