000			03082nam a22003735i 4500
001			394430
003			ES-MaUEC
005			20230102123129.0
007			cr nn 008mamaa
008			221115s2022 si | s |||| 0|eng d
020			_a9789811967030
024	7		_a10.1007/978-981-19-6703-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
100	1		_aYuan, Ye _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aLatent Factor Analysis for High-dimensional and Sparse Matrices _bA particle swarm optimization-based approach _cby Ye Yuan, Xin Luo
250			_a1st edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (VIII, 92 páginas) _b1 illus
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_aChapter 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.
520			_aLatent 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.
700	1		_aLuo, Xin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9789811967023
776	0	8	_iPrinted edition: _z9789811967047
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-6703-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Computer_2022
999			_c394430 _d394430