Characteristics of Kernel Methods
Support Vector Machines are also the most well-known learning systems based on kernel methods. The kernel representation of data amounts to a non-linear projection of data into a high-dimensional space where it is easier to separate the two classes of data.
Characteristics of Kernel Methods:
1. Embedding: Input x -. X from some input space X is embedded into a feature space F via a feature map.
2. Linear Models: It is built for the patterns in the feature space efficiently to find the optimal model, convex optimization.
3. Kernel Trick: Algorithms work with kernels, inner products of feature vectors k rather than the explicit features, side-step the efficiency problems of high-dimensionality.
4. Regularized Learning: To avoid overfitting, large feature weights are penalized, and separation by a large margin is favored.