A Gaussian Kernel is a function used in machine learning and statistics to measure similarity between data points, effectively transforming data into a higher-dimensional space. It is widely used in algorithms like Support Vector Machines and Gaussian Processes to enable non-linear classification and regression by leveraging the properties of the Gaussian distribution.

Gaussian Kernel

function used in machine learning

Function used in statistics

measure similarity between data points

transforming data into a higher-dimensional space

algorithms like Support Vector Machines

Algorithms like Gaussian Processes

Non-linear classification refers to the process of categorizing data points that cannot be separated by a straight line, using algorithms that can model complex decision boundaries. This approach is crucial for handling real-world data that often exhibits intricate patterns and relationships, beyond the capabilities of linear classifiers.

non-linear classification

Non-linear regression is a form of regression analysis where data is modeled by a function that is a non-linear combination of the model parameters and depends on one or more independent variables. It is used when the relationship between the independent and dependent variables cannot be adequately described by a straight line, allowing for more complex data patterns to be captured.

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