The sigmoid kernel is a type of kernel function used in support vector machines (SVM) that computes the similarity between two data points based on the hyperbolic tangent function. It is defined as $$K(x_i, x_j) = \tanh(\alpha x_i^T x_j + c)$$, where \(\alpha\) and \(c\) are parameters that control the shape of the kernel. This kernel helps in transforming the input space into a higher-dimensional space, allowing SVM to classify non-linear data effectively.
congrats on reading the definition of sigmoid kernel. now let's actually learn it.