Gnecco, Giorgio and Gori, Marco and Melacci, Stefano and Sanguineti, Marcello Learning with Hard Constraints. In: Artificial Neural Networks and Machine Learning – ICANN 2013. Lecture notes in computer science (8131). Springer, pp. 146153. ISBN 9783642407284 (2013)
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Abstract
A learning paradigm is proposed, in which one has both classical supervised examples and constraints that cannot be violated, called here “hard constraints”, such as those enforcing the probabilistic normalization of a density function or imposing coherent decisions of the classifiers acting on different views of the same pattern. In contrast, supervised examples can be violated at the cost of some penalization (quantified by the choice of a suitable loss function) and so play the roles of “soft constraints”. Constrained variational calculus is exploited to derive a representation theorem which provides a description of the “optimal body of the agent”, i.e. the functional structure of the solution to the proposed learning problem. It is shown that the solution can be represented in terms of a set of “support constraints”, thus extending the wellknown notion of “support vectors”.
Item Type:  Book Section 

Identification Number:  10.1007/9783642407284_19 
Additional Information:  23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 1013, 2013. Proceedings 
Uncontrolled Keywords:  Learning from constraints; learning with prior knowledge; multitask learning; support constraints; constrained variational calculus 
Subjects:  Q Science > QA Mathematics > QA75 Electronic computers. Computer science 
Research Area:  Computer Science and Applications 
Depositing User:  Giorgio Gnecco 
Date Deposited:  17 Sep 2013 12:55 
Last Modified:  18 Feb 2015 12:01 
URI:  http://eprints.imtlucca.it/id/eprint/1769 
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Learning with hard constraints. (deposited 12 Sep 2013 12:44)
 Learning with Hard Constraints. (deposited 17 Sep 2013 12:55) [Currently Displayed]
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