Concept of Feature Mapping
To deal with non-linear data, one approach could be to map the input data into a higher-dimensional space where it is linearly separable. This mapping involves transforming the data into a new space (feature space) where the separation between the data points is clearer.
For example, consider a set of data points that are not linearly separable in two dimensions. By mapping these points into a three-dimensional space, we might find that they can be separated by a plane in this higher-dimensional space.
Kernel Trick in Support Vector Classification
Support Vector Machines (SVMs) have proven to be a powerful and versatile tool for classification tasks. A key component that significantly enhances the capabilities of SVMs, particularly in dealing with non-linear data, is the Kernel Trick. This article delves into the intricacies of the Kernel Trick, its motivation, implementation, and practical applications.
Table of Content
- Linear vs Non-Linear Problems
- Concept of Feature Mapping
- What is the Kernel Trick?
- How Does the Kernel Trick Work?
- Conclusion
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