The support vector machine (SVM) is a widely used machine learning tool for classification based on statistical learning theory. Given a set of training data, the SVM finds a hyperplane that separates two different classes of data points by the largest distance. While the standard form of SVM uses L2-norm regularization, other regularization approaches are particularly attractive for biomedical datasets where, for example, sparsity and interpretability of the classifier's coefficient values are highly desired features. Therefore, in this paper we consider different types of regularization approaches for SVMs, and explore them in both synthetic and real biomedical datasets.
Lopez-Martinez, D., and Picard, R. "Multi-task Neural Networks for Personalized Pain Recognition from Physiological Signals," International Conference on Affective Computing and Intelligent Interaction (ACII) Workshop on Tools and Algorithms for Mental Health and Wellbeing, Pain, and Distress (MHWPD), San Antonio, Texas, October 2017
Lopez-Martinez D, Picard R. Continuous pain intensity estimation from autonomic signals with recurrent neural networks, in IEEE Engineering in Medicine and Biology Society (EMBC). Hawaii ; 2018.
Daniel Lopez-Martinez, Ognjen Rudovic and Rosalind W. Picard. “Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions.” 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017): 2318-2327.