Support Vector Machine SVM is a supervised learning method that uses a hyperplane as a decision plane to separate positive and negative examples. It processes data in the process of classification and regression.

Support vector machines construct hyperplanes and sets in high-dimensional or infinite-dimensional space, and then perform classification, regression or other tasks. Intuitively, the farther the classification boundary is from the nearest training data point, the better, because this can reduce the generalization error of the classifier.

SVM is a binary classification model, which can be defined as a linear classifier with the largest interval in the feature space. The learning strategy is to maximize the interval, which can generally be converted into a convex quadratic programming problem for solution.

Support Vector Machine Applications

• Classification of text and hypertext
• Image Classification
• Handwriting recognition
• Protein classification in medicine

Related words: classification, regression, margin maximization