Inductive logic programming: issues, results and the challenge of learning language in logic
Artificial Intelligence - Special issue on applications of artificial intelligence
Robust Classification for Imprecise Environments
Machine Learning
Machine Learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Shared Ensemble Learning Using Multi-trees
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
SMILES: A Multi-purpose Learning System
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
From Ensemble Methods to Comprehensible Models
DS '02 Proceedings of the 5th International Conference on Discovery Science
Beam search extraction and forgetting strategies on shared ensembles
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
| Hi-index | 0.00 |
This paper abstracts the contents of the PhD dissertation which has been recently defended by the author. Machine learning is the area of computer science that is concerned with the question of how to construct computer programs that automatically improve with experience. Recently, there have been important advances in the theoretical foundations of this field. At the same time, many successful applications have been developed: systems for extracting information from databases (data mining), applications to support decisions in medicine, telephone fraud and network intrusion detection, prediction of natural disasters, email filtering, document classification, and many others. This thesis introduces novel supervised learning methods that produce accurate and comprehensible models from past experiences which minimise the costs of generation and the costs of application.