Training Binary Classifiers as Data Structure Invariants - IEEE Xplore
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We present a technique to distinguish valid from invalid data structure objects. The technique is based on building an artificial neural network.
Training Binary Classifiers as Data Structure Invariants. Facundo Molina. Department of Computer Science, University of Rio Cuarto, Argentina. CONICET ...
The technique is based on building an artificial neural network, more precisely a binary classifier, and training it to identify valid and invalid instances of ...
In this page you can find the experiments performed as part of the paper "Training Binary Classifiers as Data Structure Invariants" published at ICSE 2019.
We present a technique to distinguish valid from invalid data structure objects. The technique is based on building an artificial neural network, ...
PDF | On May 1, 2019, Facundo Molina and others published Training Binary Classifiers as Data Structure Invariants | Find, read and cite all the research ...
Apr 1, 2012 · We show that this learning technique produces classifiers that achieve significantly better accuracy in classifying valid/invalid objects ...
Abstract: We present a technique that enables us to distinguish valid from invalid data structure objects. The technique is based on building an artificial ...
[en] We present a technique that enables us to distinguish valid from invalid data structure objects. The technique is based on building an artificial ...
So far, we have published the paper Training Binary Classifiers as Data Structure Invariants at the International Conference on Software Engineering 2019.