MATLAB Implementation of binary classification SVM

MATLAB is used to implement binary classification SVM. The optimization technology uses the quadprog function provided by Matlab.

Only to check what you have learned, more familiar; not to show off. There is not much time to use more optimization methods.

Function Model = svm0311 (data, options) % svm0311 solution 2 SVM method, optimized using the Matlab Optimization Toolbox quadprog function to implement % by lifeiteng Email: lifeiteng0422@gmail.com % reference: stptool % Pattern Recognition and machine learning p333 7.32-7.37% input aruments % ------------------------------------------- ticdata = C2S (data); [dim, num_data] = size (data. x); If nargin <2, Options = []; else Options = C2S (options); endif ~ Isfield (options, 'ker'), options. Ker = 'linear '; endif ~ Isfield (options, 'arg '), options. Arg = 1; endif ~ Isfield (options, 'C'), options. c = inf; endif ~ Isfield (options, 'norm'), options. Norm = 1; endif ~ Isfield (options, 'mu '), options. MU = 1e-12; endif ~ Isfield (options, 'eps '), options. EPS = 1e-12; endx = data. x; t = data. y; T (t = 2) =-1; % set up QP task % -------------------------- K = x' * X; t = t' * t; % note that T is a horizontal volume H = K. * t; save ('h0311. mat ', 'H') H = H + options. mu * eye (SIZE (h); F =-ones (num_data, 1); aeq = T; beq = 0; lB = zeros (num_data, 1 ); UB = options. C * ones (num_data, 1); X0 = zeros (num_data, 1); qp_options = optimset ('display', 'off'); [Alpha, fval, exitflag] = quadprog (H, F, [], [], aeq, beq, LB, UB, x0, qp_options); inx_sv = find (alpha> options. EPS); % compute bias % ------------------------ % Take boundary (f (x) = +/-1) Support Vectors 0 <Alpha <cb = 0; inx_bound = find (alpha> options. EPS & Alpha <(options. c-Options. EPS); Nm = length (inx_bound); for n = 1: Nm TMP = 0; for M = 1: length (inx_sv) % prml7.37 TMP = TMP + alpha (inx_sv (M) * t (inx_sv (M) * K (inx_bound (N), inx_sv (m )); end B = B + T (inx_bound (N)-TMP; endb = B/Nm; model. B = B; % ------------------------------------- W = zeros (dim, 1); for I = 1: num_data W = W + alpha (I) * t (I) * X (:, i); % PRML 7.29 endmargin = 1/norm (w); % ------------------------------------------- % the interface used for drawing and other models is consistent with that used by stprtool. alpha = alpha (inx_sv); Model. sv. X = data. X (:, inx_sv); Model. sv. y = data. Y (inx_sv); Model. sv. signature = inx_sv; model. nSv = length (inx_sv); Model. margin = margin; model. exitflag = exitflag; model. options = options; model. kercnt = num_data * (num_data + 1)/2; model. trnerr = cerror (data. y, svmclass (data. x, model); Model. fun = 'svmclass'; model. W = model. sv. x * model. alpha; % used CPU timemodel. cputime = toc; return;