Compartir
Regularization, Optimization, Kernels, and Support Vector Machines (en Inglés)
Suykens, Johan A. K. ; Signoretto, Marco ; Argyriou, Andreas (Autor)
·
CRC Press
· Tapa Dura
Regularization, Optimization, Kernels, and Support Vector Machines (en Inglés) - Suykens, Johan A. K. ; Signoretto, Marco ; Argyriou, Andreas
137,37 €
144,60 €
Ahorras: 7,23 €
Elige la lista en la que quieres agregar tu producto o crea una nueva lista
✓ Producto agregado correctamente a la lista de deseos.
Ir a Mis Listas
Origen: Reino Unido
(Costos de importación incluídos en el precio)
Se enviará desde nuestra bodega entre el
Jueves 04 de Julio y el
Miércoles 17 de Julio.
Lo recibirás en cualquier lugar de España entre 1 y 5 días hábiles luego del envío.
Reseña del libro "Regularization, Optimization, Kernels, and Support Vector Machines (en Inglés)"
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.
