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svm

Summary

  • Theory
  • Kernals

Theory

Support Vector Machines (SVM) are a popular machine learning algorithm used for classification tasks. They work by finding the hyperplane that best separates the data into different classes. The hyperplane is the decision boundary that maximizes the margin between the classes.

In general is the best algorithm for classification tasks. It's used to find the best hyperplane that separates the data into classes.

It's most used to complex problems like caracters recognition, image classification, voice recognition, etc.

The algortihm tries to find the best hyperplane that separates the data into classes. The hyperplane is the decision boundary that maximizes the margin between the classes.

Kernel trick

The kernel trick is a technique used to transform the data into a higher-dimensional space so that it can be linearly separable. This allows the SVM to find a hyperplane that separates the data into classes.

  • Linear Kernel
  • Polynomial Kernel
  • Gaussian Kernel
  • Tangent Hyperbolic Kernel