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Titlebook: Latent Factor Analysis for High-dimensional and Sparse Matrices; A particle swarm opt Ye Yuan,Xin Luo Book 2022 The Author(s), under exclus

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發(fā)表于 2025-3-21 19:42:44 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Latent Factor Analysis for High-dimensional and Sparse Matrices
副標(biāo)題A particle swarm opt
編輯Ye Yuan,Xin Luo
視頻videohttp://file.papertrans.cn/582/581797/581797.mp4
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
叢書名稱SpringerBriefs in Computer Science
圖書封面Titlebook: Latent Factor Analysis for High-dimensional and Sparse Matrices; A particle swarm opt Ye Yuan,Xin Luo Book 2022 The Author(s), under exclus
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
關(guān)鍵詞Latent factor analysis; High-dimensional and Sparse; Hyper-parameter-free; Particle Swarm Optimization;
版次1
doihttps://doi.org/10.1007/978-981-19-6703-0
isbn_softcover978-981-19-6702-3
isbn_ebook978-981-19-6703-0Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
The information of publication is updating

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書目名稱Latent Factor Analysis for High-dimensional and Sparse Matrices讀者反饋學(xué)科排名




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發(fā)表于 2025-3-21 20:28:20 | 只看該作者
Learning Rate-Free Latent Factor Analysis via PSO,le to obtain useful information from big data, which contain a wealth of knowledge and are high-dimensional and sparse (HiDS) [1–5], e.g., node interaction in sensor networks [6–8], user-service invoking in cloud computing [9–15], protein interaction in biological information [16–18], user interacti
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發(fā)表于 2025-3-22 19:58:46 | 只看該作者
Ye Yuan,Xin Luossachusetts becoming almost entirely female. This drastic shift in population presents a unique lens through which to study gender roles and social relations in the late nineteenth and early twentieth century. The lessons gleaned from this case study will provide new insight to the study of gender r
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發(fā)表于 2025-3-23 01:22:54 | 只看該作者
Ye Yuan,Xin Luo on gender relations in a rural setting.During the last half of the nineteenth century, a number of social and economic factors converged that resulted in the rural village of Deerfield, Massachusetts becoming almost entirely female. This drastic shift in population presents a unique lens through wh
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發(fā)表于 2025-3-23 08:40:08 | 只看該作者
Ye Yuan,Xin Luo on gender relations in a rural setting.During the last half of the nineteenth century, a number of social and economic factors converged that resulted in the rural village of Deerfield, Massachusetts becoming almost entirely female. This drastic shift in population presents a unique lens through wh
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