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Machine Learning: Linear classifier

Suppose we have 2 kernel functions $K_1(x,y)$ and $K_2(x,y)$. We know, that the dataset ($(x_1,y_1),\ldots,(x_l,y_l),$ $y_i \in \{-1,1\}$ ) is separated with the first one (that is, there are $w,$ $w_0$: $$y_i(K_1(w,x_i)-w_0)>0 $$ for all $i=1,\ldots,l$ ), and not separated with the second kernel function. What we can say about kernel function $K_1(x,y)+K_2(x,y)$ ? How I can show that the same dataset is separated with it? (Or it's false?)

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  • was there a problem in the migration process? http://stats.stackexchange.com/questions/26465/machine-learning-linear-classifier – Memming Apr 14 '12 at 22:16
  • Why do you believe the same dataset will be separated with $K_1(x,y)+K_2(x,y)$? Is this homework? – D.W. Apr 14 '12 at 23:22

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