This paper presents a novel approach for learning with constraints called Semantic-Based Regularization. This paper shows how prior knowledge in form of First Order Logic (FOL) clauses, converted into a set of continuous constraints and integrated into a learning framework, allows to jointly learn from examples and semantic knowledge. A series of experiments on artificial learning tasks and application of text categorization in relational context will be presented to emphasize the benefits given by the introduction of logic rules into the learning process. © Springer International Publishing Switzerland 2014.
|Titolo:||Experimental guidelines for semantic-based regularization|
|Citazione:||Claudio, S., Diligenti, M., & Gori, M. (2014). Experimental guidelines for semantic-based regularization. In Recent Advances of Neural Network Models and Applications - Proceedings of the 23rd Workshop of the Italian Neural Networks Society (SIREN) (pp.15-23). SPRINGER-VERLAG.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|
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