Support-vector networks

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Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “This enables a downstream decoder, such as a perceptron (Rosenblatt, 1958) or support vector machine (Cortes and Vapnik, 1995), to better classify input patterns into arbitrary associations using supervised learning.”
A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks (2023, neutral): “The goal of SVMs is to ﬁnd an appropriate, for each classiﬁcation problem, hyper layer partition of the data space (Figure 1), with the aim of correctly classifying the data while achieving the maximum possible classiﬁcation margin [19].”