Catálogo de publicaciones - libros
Machine Learning: ECML 2007: 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007. Proceedings
Joost N. Kok ; Jacek Koronacki ; Raomon Lopez de Mantaras ; Stan Matwin ; Dunja Mladenič ; Andrzej Skowron (eds.)
En conferencia: 18º European Conference on Machine Learning (ECML) . Warsaw, Poland . September 17, 2007 - September 21, 2007
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Artificial Intelligence (incl. Robotics); Algorithm Analysis and Problem Complexity; Mathematical Logic and Formal Languages; Database Management
Disponibilidad
| Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
|---|---|---|---|---|
| No detectada | 2007 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-3-540-74957-8
ISBN electrónico
978-3-540-74958-5
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
Multi-objective Genetic Programming for Multiple Instance Learning
Amelia Zafra; Sebastián Ventura
This paper introduces the use of multi-objective evolutionary algorithms in multiple instance learning. In order to achieve this purpose, a multi-objective grammar-guided genetic programming algorithm (MOG3P-MI) has been designed. This algorithm has been evaluated and compared to other existing multiple instance learning algorithms. Research on the performance of our algorithm is carried out on two well-known drug activity prediction problems, Musk and Mutagenesis, both problems being considered typical benchmarks in multiple instance problems. Computational experiments indicate that the application of the MOG3P-MI algorithm improves accuracy and decreases computational cost with respect to other techniques.
- Short Papers | Pp. 790-797
Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning
Monika Žáková; Filip Železný
Knowledge representations using semantic web technologies often provide information which translates to explicit term and predicate taxonomies in relational learning. We show how to speed up the propositionalization by orders of magnitude, by exploiting such taxonomies through a novel refinement operator used in the construction of conjunctive relational features. Moreover, we accelerate the subsequent propositional search using feature generality taxonomy, determined from the initial term and predicate taxonomies and -subsumption between features. This enables the propositional rule learner to prevent the exploration of conjunctions containing a feature together with any of its subsumees and to specialize a rule by replacing a feature by its subsumee. We investigate our approach with a deterministic top-down propositional rule learner, and propositional rule learner based on stochastic local search.
- Short Papers | Pp. 798-805