Comprehensive introduction to support vector machine (SVM) algorithm

Support vector machine (SVM) is a powerful and adaptable supervised learning algorithm used for outlier detection, regression and classification tasks. It is especially effective in high-dimensional domains and is therefore widely used in classification tasks.

The main purpose of support vector machine (SVM) is to divide the data set into a large number of classes to discover the maximum marginal hyperplane (MMH), which can be done in two steps:

First step : A support vector machine will initially iteratively build a hyperplane that best distinguishes classes.

Step 2: Then it will select the hyperplane that best separates the classes.

The dimension of the hyperplane is related to the number of features. When the number of features is 2, the hyperplane is a line. When the number of features is 3, the hyperplane becomes a two-dimensional plane.

In order to construct the hyperplane, the support vector machine (SVM) uses the extreme value vector as the support vector. The goal of SVM is to find an ideal hyperplane with large margins to discretize samples of different categories in n-dimensional space.

Python implements support vector machine (SVM) classification

Important principles of support vector machine (SVM)

1. Support vector-the data point closest to the hyperplane is called support vector. You can use support vectors to determine the dividing line.

2. Hyperplane - The space or decision plane that divides a set of items into multiple categories is called a hyperplane.

3. Margin - the distance between two lines on the nearest data points of different categories.

4. Maximum margin-the ideal hyperplane is the hyperplane with the largest margin.

Support Vector Machine (SVM) Kernel

The support vector machine kernel is a function that takes a low-dimensional input space and converts it into a high-dimensional space, that is, converts a non-separable problem into a separable one question. It is mainly used for nonlinear separation problems. Simply put, the kernel does some extremely complex data transformations and then figures out how to separate the data based on defined tags or outputs.

Advantages of Support Vector Machine (SVM)

1. Effective in high-dimensional situations

2. Very memory efficient because it uses a decision function called support Vector training point subset

3. Different kernel functions can be specified for the decision function, and custom kernels can be specified

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