n-by-n matrix of pairwise kernel values over a set of n samples
y
n-by-1 vector of response values
a
n-by-1 vector of samples weights
lambda
scalar, regularization parameter
fix.bias
set to TRUE to force the bias term to 0 (default: FALSE)
Value
A list with two elements:
[object Object],[object Object]
Details
The entries in the kernel matrix K can be interpreted as dot products
in some feature space $\phi$. The corresponding weight vector can be
retrieved via $w = \sum_i v_i \phi(x_i)$. However, new samples can be
classified without explicit access to the underlying feature space:
$$w^T \phi(x) + b = \sum_i v_i \phi^T (x_i) \phi(x) + b = \sum_i v_i K( x_i, x ) + b$$