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Wir sind gerne für Sie daLatent 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.
Produkteigenschaften
- Artikelnummer: 9789811967023
- Medium: Buch
- ISBN: 978-981-19-6702-3
- Verlag: Springer Nature Singapore
- Erscheinungstermin: 16.11.2022
- Sprache(n): Englisch
- Auflage: 1. Auflage 2022
- Serie: SpringerBriefs in Computer Science
- Produktform: Kartoniert
- Gewicht: 166 g
- Seiten: 92
- Format (B x H x T): 155 x 235 x 6 mm
- Ausgabetyp: Kein, Unbekannt