are apparently hard to separate because the values of blocks $K_2$ and $K_3$ for the two classes are close to each other. However, blocks $K_1$ and $K_4$ in the test samples are well-separated. Hence, if we are given information about these two blocks as general knowledge for the entire population, we could expect the resulting SVM classifier to perform much better on the test data. Since we know the mean values of the distributions from which the entries in $K_1$ and $K_4$ are generated, we can supply the following information about the relationship between the block sample means and class membership to the KSVM: $\frac{1}{L_1}\sum_{i\in K_1}x_i \geq 4 \Rightarrow {\textbf{x}}\in A^+$, and $\frac{1}{L_4}\sum_{i\in K_4}x_i \geq 3 \Rightarrow {\textbf{x}}\in A^-$ where $L_j$ is the length of the $K_j$, $j=1,\cdots,4$, $A^+$ and $A^-$ represent the positive and negative classes, and the lowercase $x_i$ denotes the $i$-th entry of the sample ${\textbf{x}}$. Translating into the notation of (KSVM-P), we have $$\small
  B = \left(
        \begin{array}{cccccc}
          {\textbf{0}}& \underbrace{-\frac{{\textbf{e}}}{L_1}^T } & {\textbf{0}}& {\textbf{0}}& {\textbf{0}}&{\textbf{0}}\\
          & K_1 & & & & \\
        \end{array}
      \right)
   , d = -4, \label{eq:enk_data_knowledge1} \mbox{ and }
 D = \left(
        \begin{array}{cccccc}
          {\textbf{0}}& {\textbf{0}}& {\textbf{0}}& {\textbf{0}}& \underbrace{-\frac{{\textbf{e}}}{L_4}^T }  & {\textbf{0}}\\
          & & 