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Constraint Verification With Kernel Machines

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2 Author(s)
Gori, M. ; Dept. of Inf. Eng. & Math. Sci., Univ. of Siena, Siena, Italy ; Melacci, S.

Based on a recently proposed framework of learning from constraints using kernel-based representations, in this brief, we naturally extend its application to the case of inferences on new constraints. We give examples for polynomials and first-order logic by showing how new constraints can be checked on the basis of given premises and data samples. Interestingly, this gives rise to a perceptual logic scheme in which the inference mechanisms do not rely only on formal schemes, but also on the data probability distribution. It is claimed that when using a properly relaxed computational checking approach, the complementary role of data samples makes it possible to break the complexity barriers of related formal checking mechanisms.

Published in:

Neural Networks and Learning Systems, IEEE Transactions on  (Volume:24 ,  Issue: 5 )

Date of Publication:

May 2013

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