Automated Concept Acquisition in Noisy Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Explanation and prediction: an architecture for default and abductive reasoning
Computational Intelligence
Beyond inversion of resolution
Proceedings of the seventh international conference (1990) on Machine learning
Rigel: An Inductive Learning System
Machine Learning
Guiding induction with domain theories
Machine learning
The Utility of Knowledge in Inductive Learning
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Integrated Architectures for Machine Learning
Machine Learning and Its Applications, Advanced Lectures
Resampling vs Reweighting in Boosting a Relational Weak Learner
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
A Monte Carlo Approach to Hard Relational Learning Problems
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Search-intensive concept induction
Evolutionary Computation
Handling continuous-valued attributes in incremental first-order rules learning
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Supporting clinico-genomic knowledge discovery: a multi-strategy data mining process
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Learning Classification Programs: The Genetic Algorithm Approach
Fundamenta Informaticae
| Hi-index | 0.00 |
Inducing concept descriptions in First Order Logic is inherently a complex task. There are two main reasons: on one hand, the task is usually formulated as a search problem inside a very large space of logical descriptions which needs strong heuristics to be kept to manageable size. On the other hand, most developed algorithms are unable to handle numerical features, typically occurring in realworld data. In this paper, we describe the learning system SMART+, that embeds sophisticated knowledge-based heuristics to control the search process and is able to deal with numerical features. SMART+ can use different learning strategies, such as inductive, deductive and abductive ones, and exploits both backgruond knowledge and statistical evaluation criteria. Furthermore, it can use simple Genetic Algorithms to refine predicate semantics and this aspect will be described in detail. Finally, an evaluation of SMART+ performances is made on a complex task.