Progress in Mathematical Programming Interior-point and related methods
Progress in Mathematical Programming Interior-point and related methods
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An introduction to inductive logic programming
Relational Data Mining
Learning Logical Definitions from Relations
Machine Learning
Learning the Kernel Matrix with Semi-Definite Programming
Learning the Kernel Matrix with Semi-Definite Programming
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Kernels and Distances for Structured Data
Machine Learning
On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
Integrating Naïve Bayes and FOIL
The Journal of Machine Learning Research
Learning from interpretations: a rooted kernel for ordered hypergraphs
Proceedings of the 24th international conference on Machine learning
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
l1 regularization in infinite dimensional feature spaces
COLT'07 Proceedings of the 20th annual conference on Learning theory
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In this paper l 1 regularization is introduced into relational learning to produce sparse rule combination. In other words, as few as possible rules are contained in the final rule set. Furthermore, we design a rule complexity penalty to encourage rules with fewer literals. The resulted optimization problem has to be formulated in an infinite dimensional space of horn clauses R m associated with their corresponding complexity $\mathcal{C}_m$. It is proved that if a locally optimal rule is generated at each iteration, the final obtained rule set will be globally optimal. The proposed meta-algorithm is applicable to any single rule generator. We bring forward two algorithms, namely, l 1 FOIL and l 1 Progol. Empirical analysis is carried on ten real world tasks from bioinformatics and cheminformatics. The results demonstrate that our approach offers competitive prediction accuracy while the interpretability is straightforward.