Name: Latent Factor Analysis for High-Dimensional and Sparse Matrices: A Particle Swarm Optimization-Based Approach (en Inglés)
SKU: 59459011
Price: 55.33 USD
Availability: InStock
Author: Yuan, Ye ; Luo, Xin
ISBN: 9789811967023

portada Latent Factor Analysis for High-Dimensional and Sparse Matrices: A Particle Swarm Optimization-Based Approach (en Inglés)

Latent Factor Analysis for High-Dimensional and Sparse Matrices: A Particle Swarm Optimization-Based Approach (en Inglés)

Formato

Libro Físico

Autor

Ye Yuan

Xin Luo

Editorial

Springer

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.6x0.5 cm

Peso

0.15 kg.

ISBN13

9789811967023

Categorías

Teoría de la información
Probabilidad y estadística
Inteligencia artificial
Arquitectura de la información

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

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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.