The following Tianjin java training introduces the specific methods of different machine learning.
What is a support vector machine? Support vector machine is a machine learning algorithm, which can be used for classification and regression problems.
It uses a method called core technology to transform data and find the boundary between possible outputs according to the transformation.
In short, Beida Jade Bird found that it can perform very complicated data conversion and divide data according to defined labels or outputs.
The dominant support vector machine of support vector machine can not only classify but also play the role of regression. It can be said that it is a nonlinear support vector machine, and it can also be said that it is a support vector machine using a nonlinear kernel.
The calculation boundary of nonlinear support vector machine algorithm is not necessarily a straight line.
The advantage of Tianjin UI design is that it can capture more complex relationships between data points.
Therefore, there is no need to make complicated transformations.
The disadvantage is that it needs more calculation, so it needs longer training time.
What are the core skills? The data that can be converted by nuclear technology has several excellent characteristics, which can be used as a classifier to get data that you don't know.
It's like unraveling the DNA chain.
First, start with this invisible data vector.
When using the core prompt, it will be decrypted and self-synthesized, resulting in a large data set that even spreadsheets can't understand.
However, with the development of big data, it is found that with the expansion of data sets, the boundaries between classes become clear, and SVM algorithm can calculate a more optimized hyperplane.