ReLU

Softmax

ELU

Sigmoid

Bias

Intercept

Weights

Regularization Parameter

Gamma

C

Degree

Coefficient

Random search

Grid search

Gradient descent

Evolutionary algorithms

Over-sampling the minority class

Under-sampling the majority class

Cost-sensitive learning

Ensemble learning

It always passes through the origin

It is perpendicular to the support vectors

It is a straight line in linear SVM

It is determined by the kernel function

It is not suitable for large datasets

It is sensitive to the choice of kernel function

It cannot handle imbalanced datasets

It is prone to overfitting with small datasets

It is computationally efficient for large datasets

It may lead to overfitting with small datasets

It cannot handle non-linearly separable data

It is not suitable for noisy data

They are the data points that are closest to the decision boundary

They are the data points that are furthest from the decision boundary

They are the data points that have non-zero coefficients in the SVM solution

They are the data points that are misclassified by the SVM

It is less computationally efficient than other kernel functions

It is more prone to overfitting with small datasets

It is not suitable for non-linearly separable data

It requires more tuning of hyperparameters compared to other kernel functions