|
|
|
|
| LEADER |
02724nmm a2200337 u 4500 |
| 001 |
EB002134776 |
| 003 |
EBX01000000000000001272833 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
221201 ||| eng |
| 020 |
|
|
|a 9789811967030
|
| 100 |
1 |
|
|a Yuan, Ye
|
| 245 |
0 |
0 |
|a Latent Factor Analysis for High-dimensional and Sparse Matrices
|h Elektronische Ressource
|b A particle swarm optimization-based approach
|c by Ye Yuan, Xin Luo
|
| 250 |
|
|
|a 1st ed. 2022
|
| 260 |
|
|
|a Singapore
|b Springer Nature Singapore
|c 2022, 2022
|
| 300 |
|
|
|a VIII, 92 p. 1 illus
|b online resource
|
| 505 |
0 |
|
|a 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
|
| 653 |
|
|
|a Artificial intelligence / Data processing
|
| 653 |
|
|
|a Data Analysis and Big Data
|
| 653 |
|
|
|a Quantitative research
|
| 653 |
|
|
|a Data mining
|
| 653 |
|
|
|a Data Mining and Knowledge Discovery
|
| 653 |
|
|
|a Data Science
|
| 700 |
1 |
|
|a Luo, Xin
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a SpringerBriefs in Computer Science
|
| 028 |
5 |
0 |
|a 10.1007/978-981-19-6703-0
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-19-6703-0?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 005.7
|
| 520 |
|
|
|a 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
|