Kernel Method
2024 | AI Dictionary
What is Kernel Method: A machine learning technique that enables algorithms to learn in higher-dimensional spaces without explicitly computing transformations.
What is Kernel Method?
The Kernel Method is a technique used in machine learning to enable algorithms to learn in higher-dimensional spaces without explicitly computing the transformation. It relies on the kernel trick, which allows algorithms like Support Vector Machines (SVM) to compute inner products in transformed feature spaces without directly calculating the transformation, thus improving efficiency.
Types of Kernel Functions
- Linear Kernel: Computes the inner product of two vectors directly.
- Polynomial Kernel: Computes a polynomial of the inner product, enabling the learning of non-linear boundaries.
- Gaussian (RBF) Kernel: Uses the distance between data points and applies a Gaussian function, commonly used in SVM and clustering .
Applications of Kernel Method
- Support Vector Machines (SVM): Helps SVM perform classification in non-linear feature spaces.
- Principal Component Analysis (PCA) : Used in Kernel PCA for non-linear dimensionality reduction .
- Clustering: Applied in algorithms like Spectral Clustering for non-linear cluster separation.
Example of Kernel Method
An example is the Gaussian Kernel in SVMs, where the kernel function maps the data into a higher-dimensional space where a linear hyperplane can separate data points that were originally non-linearly separable.
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