: Vocabulaire du TAL: support vector machine

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support vector machine

A supervised machine learning model for data classification and regression analysis which optimizes the width of the gap between the points of separate categories in feature space. (Adapted from NIST, The Language of Trustworthy AI: An In-Depth Glossary of Terms, 2023)

A linear Support Vector Machine is used for classification and feature vectors are created using relative frequency values. (Malmasi & Dras, 2015)
Finally Support vector machines (SVMs) were used to predict acoustic variations for all the leaves of main tree (at word and syllable layers) and subtrees (at phone layer). (Ai, 2013)
On the other hand SVM reached high performances on almost all the evaluation metrics of the models trained. (Beccaria, Gagliardi & Kokkinakis, 2022)
The regularization parameter for the SVM was found to be C = 2 based on cross-validation experiments to maximize the final recall. (Kayal, Afzal, Tsatsaronis, Katrenko, Coupet, Doornenbal & Gregory, 2017)
The SVM was trained on the examples of positive and negative segments i.e. paragraphs with and without funding information which could be found in the "gold" set described in the previous section. (Kayal, Afzal, Tsatsaronis, Katrenko, Coupet, Doornenbal & Gregory, 2017)

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