Table 3. Weighted Kernel-based Iterative Estimation of Relevance Algorithm (wKIERA).

Algorithm wKIERA
Inputs
Dataset: D = {(x J,y J)}, J = {1,…,m}; Base kernel: kerbase;
Pool size: poolsize; Max. iterations: maxiter;
Output
bestw
Algorithm
n = dim(x 1);
W = rand_matrix_01 (poolsize,n)
repeat for (t = 1, top = 0; t<maxiter; t++)
[S,U] = random_split (J,n/2)
repeat for each row w in W
K = compute_wkernel (w,x J,kerbase)
h = train_kperceptron(KS,y S)
scorew = 0.99*test_kperceptron (h,KU,y U)+0.01*len(w)
if(scorew>top)
top = scorew; bestw = w;
end_if
end_repeat
B = select_half_best (W,scorei = 1:poolsize);
μ = mean(B); σ = std_dev(B);
[δ,ξ] = skewness_schedule (t,top);
Wnew = μ+((σ+ξ)*rand_matrix_skewed_01 (poolsize,n,δ))
Wnew 1 = bestw; W = Wnew
end_repeat