Below, we focus on a kernel-based classifier trained with a polynomial kernel of degree d (here, 1 For example, given an input vector x = (x1, x2) T and a support vector In the following argument, we ignore the dimension of the constant in the mapped space and assume constant b is set to include it.
2 speaking, they set different thresholds to positive (αj > 0) and negative (αj < 0) support vectors, considering the proportion of positive and negative support vectors.