| 书目名称 | Latent Factor Analysis for High-dimensional and Sparse Matrices | | 副标题 | A particle swarm opt | | 编辑 | Ye Yuan,Xin Luo | | 视频video | | | 概述 | Offers a comprehensive introduction to latent factor analysis on high-dimensional and sparse data.Presents an effective hyper-parameter adaptation method for latent factor analysis models.Outlines an | | 丛书名称 | SpringerBriefs in Computer Science | | 图书封面 |  | | 描述 | 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.. | | 出版日期 | Book 2022 | | 关键词 | Latent factor analysis; High-dimensional and Sparse; Hyper-parameter-free; Particle Swarm Optimization; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-19-6703-0 | | isbn_softcover | 978-981-19-6702-3 | | isbn_ebook | 978-981-19-6703-0Series ISSN 2191-5768 Series E-ISSN 2191-5776 | | issn_series | 2191-5768 | | copyright | The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 |
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发表于 2025-3-21 19:42:44
