Catálogo de publicaciones - libros
Validated Designs for Object-oriented Systems
John Fitzgerald Peter Gorm Larsen Paul Mukherjee Nico Plat Marcel Verhoef
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Programming Techniques; Software Engineering/Programming and Operating Systems; Software Engineering; Discrete Mathematics in Computer Science
Disponibilidad
| Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
|---|---|---|---|---|
| No detectada | 2005 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-1-85233-881-7
ISBN electrónico
978-1-84628-107-5
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2005
Información sobre derechos de publicación
© Springer-Verlag London Limited 2005
Cobertura temática
Tabla de contenidos
TradeOne: From Enterprise Architecture to Business Application
John Fitzgerald; Peter Gorm Larsen; Paul Mukherjee; Nico Plat; Marcel Verhoef
A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.
Part III - Modelling in Practice: Three Case Studies | Pp. 249-272
Concurrency in VDM++
John Fitzgerald; Peter Gorm Larsen; Paul Mukherjee; Nico Plat; Marcel Verhoef
A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.
Part IV - From Models to Code | Pp. 275-294
Model Quality
John Fitzgerald; Peter Gorm Larsen; Paul Mukherjee; Nico Plat; Marcel Verhoef
A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.
Part IV - From Models to Code | Pp. 295-318
Implementing in Java
John Fitzgerald; Peter Gorm Larsen; Paul Mukherjee; Nico Plat; Marcel Verhoef
A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.
Part IV - From Models to Code | Pp. 319-345