Technical Note: \cal Q-Learning
Machine Learning
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Inductive logic programming: issues, results and the challenge of learning language in logic
Artificial Intelligence - Special issue on applications of artificial intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Relational Data Mining
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Incremental Learning of Functional Logic Programs
FLOPS '01 Proceedings of the 5th International Symposium on Functional and Logic Programming
What Is a Learning Classifier System?
Learning Classifier Systems, From Foundations to Applications
Optimal Ordered Problem Solver
Machine Learning
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Proceedings of the third ACM SIGPLAN conference on History of programming languages
Structured machine learning: the next ten years
Machine Learning
Structured prediction with reinforcement learning
Machine Learning
Web Categorisation Using Distance-Based Decision Trees
Electronic Notes in Theoretical Computer Science (ENTCS)
A genetic algorithms approach to ILP
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
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In this paper, we push forward the idea of machine learning systems for which the operators can be modified and finetuned for each problem. This allows us to propose a learning paradigm where users can write (or adapt) their operators, according to the problem, data representation and the way the information should be navigated. To achieve this goal, data instances, background knowledge, rules, programs and operators are all written in the same functional language, Erlang. Since changing operators affect how the search space needs to be explored, heuristics are learnt as a result of a decision process based on reinforcement learning where each action is defined as a choice of operator and rule. As a result, the architecture can be seen as a …system for writing machine learning systems' or to explore new operators.