Table of contents
Support Vector Machine SVM Detailed Principles
SVM Definition
SVM Theory
SVM Application Examples
SVM Parameter Optimization
SVM Regression Analysis
Code
Result Analysis
Outlook
The detailed principle of support vector machine SVM
Definition of SVM
Support vector machines (SVM) is a binary classification model whose basic model is a linear classifier with the largest interval defined in the feature space. The largest interval makes it different from the perceptron; SVM also includes kernel techniques , which makes it a substantially nonlinear classifier. The learning strategy of SVM is to maximize the interval, which can be formalized as a problem of solving convex quadratic programming, which is also equivalent to the problem of minimizing the regularized hinge loss function. The learning algorithm of SVM is the optimization algorithm for solving convex quadratic programming.
(1) Support Vector Machine (SVM) is a generalized linear classifier for binary classification of data, and its classification boundary is the maximum interval hyperplane for solving the learning samples.
(2) SVM uses the hinge loss function to calculate empirical risk and adds a regularization term to the solution system to optimize structural risk. It is a classifier with sparsity and robustness.
(3) SVM can perform nonlinear classification by introducing kernel functions.
SVM theory
1. Linear separability
2. Loss function