株式会社極東書店トップ ＞ 商品一覧 ＞ Regularization, Optimization, Kernels, and Support Vector Machines.
商品詳細
Regularization, Optimization, Kernels, and Support Vector Machines.
・ISBN 978-0-367-65898-4 paper GB£ 49.99
￥15,836.- (税込) ※（※）価格はご注文時の参考価格となります。
納品価格につきましては書籍の入荷時点で確定となります。
版元の原価改定、外国為替の変動等により異なる場合がございますので、予めご了承下さい。
| 著者・編者 | Suykens, Johan A.K. / Signoretto, Marco / Argyriou, Andreas (eds.), |
|---|---|
| シリーズ | (Chapman & Hall/CRC Machine Learning & Pattern Recognition) |
| 出版社 | (Chapman & Hall/CRC, UK) |
| 出版年月 | 2020 |
| ページ数 | 525 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A00-47329＞ |
解説
Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference:
- Covers the relationship between support vector machines (SVMs) and the Lasso
- Discusses multi-layer SVMs
- Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing
- Describes graph-based regularization methods for single- and multi-task learning
- Considers regularized methods for dictionary learning and portfolio selection
- Addresses non-negative matrix factorization
- Examines low-rank matrix and tensor-based models
- Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing
- Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent
Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.