Catálogo de publicaciones - libros
Learning Classifier Systems: International Workshops, IWLCS 2003-2005, Revised Selected Papers
Tim Kovacs ; Xavier Llorà ; Keiki Takadama ; Pier Luca Lanzi ; Wolfgang Stolzmann ; Stewart W. Wilson (eds.)
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Artificial Intelligence (incl. Robotics); Computation by Abstract Devices; Mathematical Logic and Formal Languages; Data Mining and Knowledge Discovery
Disponibilidad
| Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
|---|---|---|---|---|
| No detectada | 2007 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-3-540-71230-5
ISBN electrónico
978-3-540-71231-2
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2007
Información sobre derechos de publicación
© Springer-Verlag Berlin Heidelberg 2007
Tabla de contenidos
Using XCS to Describe Continuous-Valued Problem Spaces
David Wyatt; Larry Bull; Ian Parmee
Learning classifier systems have previously been shown to have some application in single-step tasks. This paper extends work in the area by applying the classifier system to progressively more complex multi-modal test environments, each with typical search space characteristics, convex/non-convex regions of high performance and complex interplay between variables. In particular, two test environments are used to investigate the effects of different degrees of feature sampling, parameter sensitivity, training set size and rule subsumption. Results show that XCSR is able to deduce the characteristics of such problem spaces to a suitable level of accuracy. This paper provides a foundation for the possible use of XCS as an exploratory tool that can provide information from conceptual design spaces enabling a designer to identify the best direction for further investigation as well as a better representation of their design problem through redefinition and reformulation of the design space.
IV - Application-Oriented Research and Tools | Pp. 308-332
The EpiXCS Workbench: A Tool for Experimentation and Visualization
John H. Holmes; Jennifer A. Sager
The EpiXCS Workbench is a knowledge discovery tool that provides the user with the capability for knowledge discovery and visualization in medical data. The foundation for the workbench is the XCS paradigm [1]. The workbench is designed to benefit both expert learning classifier systems (LCS) researchers and inexperienced end-users in a variety of domains, especially clinical, epidemiologic, and public health researchers. It was implemented in Microsoft Visual C++, Version 6.0, using the GNU Scientific Library, using the XCSlib class library developed by Lanzi [2]. EpiXCS is designed to run on Intel Pentium processor environments at 1.0GHz and higher. No special graphics or other co-processors are required.
IV - Application-Oriented Research and Tools | Pp. 333-344