Detalles del recurso
Pertenece a:
DSpace at MIT
Descripción: We present GURLS, a toolbox for supervised learning based on the regularized least squares algorithm. The toolbox takes advantage of all the favorable properties of least squares and is tailored to deal in particular with multi-category/multi-label problems. One of the main advantages of GURLS is that it allows training and tuning a multi-category classifier at essentially the same cost of one single binary classifier. The toolbox provides a set of basic functionalities including different training strategies and routines to handle computations with very large matrices by means of both memory-mapped storage and distributed task execution. The system is modular and can serve as a basis for easily prototyping new algorithms. The toolbox is available for download, easy to set-up and use.
Autor(es): Tacchetti, Andrea - Mallapragada, Pavan S. - Santoro, Matteo - Rosasco, Lorenzo -
Id.: 55011691
Idioma:
en-US
-
Versión: 1.0
Estado: Final
Tipo: 6 p. -
Palabras clave: Matlab -
Tipo de Interactividad: Expositivo
Nivel de Interactividad: muy bajo
Audiencia:
Estudiante
- Profesor
- Autor
-
Estructura: Atomic
Coste: no
Copyright: sí
Formatos: 6 p. -
Requerimientos técnicos: Browser: Any -
Relación:
[References] MIT-CSAIL-TR-2012-003
[References] CBCL-306
Fecha de contribución: 08-feb-2012
Contacto:
