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[null,"1.","Record Nr.",null,"UNINA9910631083703321"]
[null,null,"Autore",null,"Yuan Ye"]
[null,null,"Titolo",null,"Latent Factor Analysis for High-dimensional and Sparse Matrices : A particle swarm optimization-based approach / / by Ye Yuan, Xin Luo"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"9789811967030"]
[{"$nulls":4},"9811967032"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (99 pages)"]
[null,null,"Collana",null,"SpringerBriefs in Computer Science, , 2191-5776"]
[null,null,"Disciplina",null,"519.535"]
[null,null,"Soggetti",null,"Artificial intelligence - Data processing"]
[{"$nulls":4},"Quantitative research"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Data Science"]
[{"$nulls":4},"Data Analysis and Big Data"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Chapter 1. Introduction -- Chapter 2. Learning rate-free Latent Factor Analysis via PSO -- Chapter 3. Learning Rate and Regularization Coefficient-free Latent Factor Analysis via PSO -- Chapter 4. Regularization and Momentum Coefficient-free Non-negative Latent Factor Analysis via PSO -- Chapter 5. Advanced Learning rate-free Latent Factor Analysis via P2SO -- Chapter 6. Conclusion and Discussion."]
[null,null,null,"Sommario/riassunto","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 "]
[{"$nulls":4},"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."]