Models & Architectures · Level 2 of 5
Support Vector Machine
A model that learns a separating boundary using a margin-based objective.
Kernels can express nonlinear boundaries through implicit feature spaces.
Example
A classifier seeks a boundary with a large separation margin.
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Support Vector Machine. A model that learns a separating boundary using a margin-based objective. Kernels can express nonlinear boundaries through implicit feature spaces. For example: A classifier seeks a boundary with a large separation margin.
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This helps you distinguish a model’s structural assumptions from its learned parameter values.
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