Support vector machine

Support vector machine

1. is a learning model

   • associated learning algorithms that analyze data used for classification and regression analysis
     • used in industrial applications
   • Classifying data is a common task in machine learning
     • decide which class a new data point will be in
     • Whereas the original problem may be stated in a finite dimensional space

2. marked as belonging to one or the other of two categories

   • an efficiently perform a non-linear classification using what is called the kernel trick
     • find natural clustering of the data to groups
     • find natural clustering of the data to groups

3. choice as the best hyperplane is the one that represents the largest separation

   • used for classification, regression, or other tasks
     • Is classified in linear and non-linear
       • the non-lineal
         • It is noteworthy that working in a higher-dimensional
           • increases the generalization error of support vector machines
       • inean, We are given a training dataset of {\displaystyle n} n points
         • We want to find the "maximum-margin hyperplane" that divides the group of points
           • which is defined so that the distance between the hyperplane and the nearest point

4. SVC is a similar method that also builds on kernel functions but is appropriate for unsupervised learning and data-mining

   • It is considered a fundamental method in data science