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

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
General Classification 157 25.86%
Feature Selection 25 4.12%
Clustering 22 3.62%
Sentiment Analysis 20 3.29%
Image Classification 19 3.13%
Text Classification 17 2.80%
EEG 16 2.64%
Dimensionality Reduction 13 2.14%
Anomaly Detection 12 1.98%

Components


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

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