Latent Factor Analysis for High-dimensional and Sparse Matrices: A particle swarm optimization-based approach
Ye Yuan, Xin Luo
Informasi Umum
Kode
23.21.2141
Klasifikasi
004 - Data processing, Computer science
Jenis
Buku - Elektronik (E-Book)
Subjek
Computer Science
No. Rak
1
Dilihat
6 kali
Informasi Lainnya
Abstraksi
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.
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
Anda harus log in untuk mengakses flippingbook
Pengarang
| Nama | Ye Yuan, Xin Luo |
| Jenis | Perorangan |
| Penyunting | |
| Penerjemah |
Penerbit
| Nama | Springer Singapore |
| Kota | New York |
| Tahun | 2022 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
