Dorian Šuc and Ivan Bratko (2005) Combining Learning Constraints and Numerical Regression. In: 19th Int. Joint Conf. on Artificial Intelligence, IJCAI-05, 30 July - 5 August 2005, Edinburgh, Scotland.
Usual numerical learning methods are primarily concerned with finding a good numerical fit to data and often make predictions that do not correspond to qualitative laws in the domain of modelling or expert intuition. In contrast, the idea of $Q^2$ learning is to induce qualitative constraints from training data, and use the constraints to guide numerical regression. The resulting numerical predictions are consistent with a learned qualitative model which is beneficial in terms of explanation of phenomena in the modelled domain, and can also improve numerical accuracy. This paper proposes a method for combining the learning of qualitative constraints with an arbitrary numerical learner and explores the accuracy and explanation benefits of learning monotonic qualitative constraints in a number of domains. We show that $Q^2$ learning can correct for errors caused by the bias of the learning algorithm and discuss the potentials of similar hierarchical learning schemes.
|Item Type: ||Conference or Workshop Item (Paper)|
|Keywords: ||machine learning, qualitative reasoning, combining classifiers, monotonicity constraints|
|Language of Content: ||English|
|Related URLs: |
|Institution: ||University of Ljubljana|
|Department: ||Faculty of Computer and Information Science|
|Divisions: ||Faculty of Computer and Information Science > Artificial Intelligence Laboratory|
|Item ID: ||179|
|Date Deposited: ||11 Aug 2005|
|Last Modified: ||05 Dec 2013 15:18|
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