Web: http://arxiv.org/abs/2201.09928

Jan. 26, 2022, 2:10 a.m. | Luca Bortolussi, Giuseppe Maria Gallo, Jan Křetínský, Laura Nenzi

cs.LG updates on arXiv.org arxiv.org

We introduce a similarity function on formulae of signal temporal logic
(STL). It comes in the form of a kernel function, well known in machine
learning as a conceptually and computationally efficient tool. The
corresponding kernel trick allows us to circumvent the complicated process of
feature extraction, i.e. the (typically manual) effort to identify the decisive
properties of formulae so that learning can be applied. We demonstrate this
consequence and its advantages on the task of predicting (quantitative)
satisfaction of …

arxiv kernel learning model processes signal stochastic

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