Linear vs Non-Linear Problems
For linearly separable data, finding this hyperplane is straightforward. However, many real-world problems are non-linear, meaning that no linear separation can perfectly divide the classes. This is where the kernel trick comes into play.
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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