functionI = FisherInformation(b_0, b_1, x_i) I = zeros(2, 2); fori = 1:length(x_i) eta_i = b_0 + b_1 * x_i(i); p_i = normcdf(eta_i); phi_i = normpdf(eta_i); % Clip probabilities to avoid division by zero ...
The SVM algorithm classifies a new observation z using sign(ˆf(z)). In some cases, a nonlinear boundary separates the classes. Nonlinear SVM works in a transformed predictor space to find an optimal, separating hyperplane. The dual formalization for nonlinear SVM is 0.5n∑j=1n∑k=1αjα...