# MATLAB Implementation of binary classification SVM

Source: Internet
Author: User
Tags 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;`

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