SVM incremental learning, adaptation and optimization

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Support vector machines under adversarial label contamination (2015, methodology): “It is formulated under the assumption that the attacker can maliciously manipulate the set of labels to maximize the empirical loss of the original classifier on the tainted dataset, while the classification algorithm preserves its generalization on the tainted dataset without noticing it.”
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (2015, methodology): “We ask to refer to (Diehl & Cauwenberghs, 2003; Cauwenberghs & Poggio, 2000) for more details.”
Incremental learning for ν-Support Vector Regression (2015, methodology): “That is After the critical adjustment quantity ∆ η max is determined, we can update η, α, g , S S , S E and S R , similarly to the approaches in Diehl and Cauwenberghs (2003).”
The Entire Regularization Path for the Support Vector Machine (2004, background): “[14] allow for updating the parameters as well, but again do not construct entire solution paths.”
FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices (2020, methodology): “Then, for each CP, we calculate feature statistics and feed them into an incremental SVM classifier [29,58] implemented using stochastic gradient descent optimization and RBF kernel approximation by scikit-learn libraries [91].”