Non-Parametric Regression

Support Vector Machine

A Support Vector Machine, or SVM, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs to high-dimensional feature spaces. SVMs construct a hyper-plane or set of hyper-planes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyper-plane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. The figure to the right shows the decision function for a linearly separable problem, with three samples on the margin boundaries, called “support vectors”.

Source: scikit-learn


Paper Code Results Date Stars


Task Papers Share
General Classification 94 15.96%
BIG-bench Machine Learning 80 13.58%
EEG 21 3.57%
Anomaly Detection 17 2.89%
Image Classification 15 2.55%
Sentiment Analysis 14 2.38%
Text Classification 12 2.04%
Time Series 12 2.04%
Emotion Recognition 11 1.87%


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign