A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative system identification: deriving structure from behavior
Artificial Intelligence
Learning Qualitative Models of Dynamic Systems
Machine Learning - special issue on inductive logic programming
Artificial Intelligence Review - Special issue on lazy learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Semi-quantitative system identification
Artificial Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Computers and Biomedical Research
Qualitative reverse engineering
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Incremental Learning of Linear Model Trees
Machine Learning
Combining learning constraints and numerical regression
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Qualitative phase portrait of modified Black-Scholes model
Expert Systems with Applications: An International Journal
Learning qualitative models from numerical data
Artificial Intelligence
Improving vehicle aeroacoustics using machine learning
Engineering Applications of Artificial Intelligence
Qualitative Reasoning Approach to a Driver's Cognitive Mental Load
International Journal of Software Science and Computational Intelligence
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We describe an approach to machine learning from numerical data that combines both qualitative and numerical learning. This approach is carried out in two stages: (1) induction of a qualitative model from numerical examples of the behaviour of a physical system, and (2) induction of a numerical regression function that both respects the qualitative constraints and fits the training data numerically. We call this approach Q2 learning, which stands for Qualitatively faithful Quantitative learning. Induced numerical models are "qualitatively faithful" in the sense that they respect qualitative trends in the learning data. Advantages of Q2 learning are that the induced qualitative model enables a (possibly causal) explanation of relations among the variables in the modelled system, and that numerical predictions are guaranteed to be qualitatively consistent with the qualitative model which alleviates the interpretation of the predictions. Moreover, as we show experimentally the qualitative model's guidance of the quantitative modelling process leads to predictions that may be considerably more accurate than those obtained by state-of-the-art numerical learning methods. The experiments include an application of Q2 learning to the identification of a car wheel suspension system--a complex, industrially relevant mechanical system.