Do you know what Support Vector Machine SVM means?

Super layman's explanation: Support vector machines are used to solve classification problems. Consider the simplest case first, peas and rice grains, which can be separated quickly with a sun, with small particles leaking down and large particles retained.

It is expressed by a function that when the diameter d is greater than a certain value d, it is judged to be a pea, and less than a certain value is a grain of rice. D>d, Pea D<>

SVM - Support Vector Machine.

It is a supervised learning algorithm and belongs to the category of classification. in data mining.

, which corresponds to and differs from unsupervised clustering.

Widely used in machine learning.

machine learning), computer vision.

computer vision) and data mining. Suppose we want to divide the solid circle and the hollow circle into two categories through the 38th parallel. Then there are countless more lines that can accomplish this task.

In SVM, we look for an optimal dividing line.

Make it the largest margin to both sides.

In this case, the few data points with bold edges are called support vectors, which is also the name of this classification algorithm. we got a bunch of data points in a n- dimensional to infinite-dimensional space, then one can always find a optimal hyperplane which is always in the n-1 dimension.Let me give you an example, when you give SVM a piece of text, like "This phone screen is big, I like it", and you want to know whether the emotional tendency of this text is positive or negative, you throw this text to the SVM classifier, and the SVM will tell you that its emotions are positive.

But now we have one more option, "neutral". <

SVM - Support Vector Machine, commonly known as Support Vector Machine, is a supervised learning algorithm that belongs to the category of classification. In the application of data mining, it corresponds to and differs from clustering of unsupervised.

It is widely used in machine learning, computer vision and data mining.

Suppose you want to divide the solid circle and the hollow circle into two categories by the 38th line, then there are countless lines that can accomplish this task. In SVM, finding an optimal dividing line makes it the largest margin on both sides.

The SVM maps the vectors into a higher-dimensional space where a maximally spaced hyperplane is established. Two parallel hyperplanes are built on either side of the hyperplane that separates the data. Dividing the hyperplane maximizes the distance between two parallel hyperplanes.

It is assumed that the greater the distance or gap between parallel hyperplanes, the smaller the total error of the classifier.

It is a supervised learning method that is widely used in statistical classification as well as regression analysis.

C is the penalty coefficient, which is understood as the weight of the preference of the two indicators (interval size and classification accuracy) in the optimization direction, that is, the tolerance for error, the higher the c, the more intolerant the error and easy to overfit, the smaller the c, the easier it is to underfit, the smaller the c, the easier it is to underfit, the C is too large or too small, and the generalization ability becomes worse.

gamma is a parameter that comes with the rbf function after the kernel is selected. The larger the gamma, the smaller the support file positive vector, and the smaller the gamma value, the more support vectors. The number of support vectors affects the speed of training and **.