New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Logical settings for concept-learning
Artificial Intelligence
Top-down induction of first-order logical decision trees
Artificial Intelligence
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Information Retrieval
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Inference for the Generalization Error
Machine Learning
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Nowadays, many organizations have relational databases with millions of records and an important question is how to extract information from them. This work proposes HTILDE (Hoeffding TILDE) to handle very large relational databases, based on the Inductive Logic Programming (ILP) system TILDE (Top-down Induction of Logical Decision Trees) and the propositional Very Fast Decision Tree (VFDT) learner. It is an incremental and anytime algorithm that uses the Hoeffding bound to find out the amount of examples that must be considered for choosing the best test for a node. The results show that, compared to TILDE, HTILDE generates theories from very large relational datasets more efficiently without harming their quality measures (F-measure, precision, recall and accuracy). Also, HTILDE learns less complex theories than TILDE.