function net = svm(nin, kernel, kernelpar, C, use2norm, qpsolver, qpsize)% SVM - Create a Support Vector Machine classifier% %   NET = SVM(NIN, KERNEL, KERNELPAR, C, USE2NORM, QPSOLVER, QPSIZE)%   (All parameters from KERNELPAR on are optional).%   Initialise a structure NET containing the basic settings for a Support%   Vector Machine (SVM) classifier. The SVM is assumed to have input of%   dimension NIN, it works with kernel function KERNEL. If the kernel%   function needs extra parameters, these must be given in the array%   KERNELPAR. See function SVMKERNEL for a list of valid kernel%   functions.%%   The structure NET has the following fields:%   Basic SVM parameters:%     'type' = 'svm'%     'nin' = NIN   number of input dimensions%     'nout' = 1   number of output dimensions%     'kernel' = KERNEL   kernel function%     'kernelpar' = KERNELPAR   parameters for the kernel function%     'c' = C  Upper bound for the coefficients NET.alpha during%       training. Depending on the size of NET.c, the value is%       interpreted as follows:%       LENGTH(NET.c)==1: Upper bound for all coefficients.%       LENGTH(NET.c)==2: Different upper bounds for positive (+1) and%       negative (-1) examples. NET.c(1) is the bound for the positive,%       NET.c(2) is the bound for the negative examples.%       LENGTH(NET.c)==N, where N is the number of examples that are%       passed to SVMTRAIN: NET.c(i) is the upper bound for the%       coefficient NET.alpha(i) associated with example i.%       Default value: 1%     'use2norm' = USE2NORM: If non-zero, the training procedure will use%       an objective function that involves the 2norm of the errors on%       the training points, otherwise the 1norm is used (standard%       SVM). Default value: 0.%%   Fields that will be set during training with SVMTRAIN:%     'nbexamples' = Number of training examples%     'alpha' = After training, this field contains a column vector with%       coefficients (weights) for each training example. NET.alpha is%       not used in any subsequent SVM routines, it can be removed after%       training.%     'svind' = After training, this field contains the indices of those%       training examples that are Support Vectors (those with a large%       enough value of alpha)%     'sv' = Contains all the training examples that are Support Vectors.%     'svcoeff' = After training, this field is the product of NET.alpha%       times the label of the corresponding training example, for all%       examples that are Support Vectors. It is given in the same order%       as the examples are given in NET.sv.%     'bias' = The linear term of the SVM decision function.%     'normalw' = Normal vector of the hyperplane that separates the%       examples. This is only computed if a linear kernel%       NET.kernel='linear' is used.%%   Parameters specifically for SVMTRAIN (rarely need to be changed):%     'qpsolver' = QPSOLVER. QPSOLVER must be one of 'quadprog', 'loqo',%       'qp' or empty for auto-detect. Name of the function that solves%       the quadratic programming problems in SVMTRAIN.%       Default value: empty (auto-detect).%     'qpsize' =  QPSIZE. The maximum number of points given to the QP%       solver. Default value: 50.%     'alphatol' = Tolerance for all comparisons that involve the%       coefficients NET.alpha. Default value: 1E-2.%     'kkttol' = Tolerance for checking the KKT conditions (termination%       criterion) Default value: 5E-2. Lower this when high precision is%       required.%%   See also:%   SVMKERNEL, SVMTRAIN, SVMFWD%% % Copyright (c) Anton Schwaighofer (2001)% $Revision: 1.6 $ $Date: 2002/01/07 19:51:49 $% mailto:anton.schwaighofer@gmx.net% % This program is released unter the GNU General Public License.% if nargin < 7,  qpsize = 50;endif nargin < 6,  qpsolver = '';endif nargin < 5,  use2norm = 0;endif nargin < 4,  C = 1;endif nargin < 3,  kernelpar = [];endnet.type = 'svm';net.nin = nin;net.nout = 1;net.kernel = kernel;net.kernelpar = kernelpar;net.c = C;net.use2norm = use2norm;net.nbexamples = 0;net.alpha = [];net.svcoeff = [];net.sv = [];net.svind = [];net.bias = [];net.normalw = [];net.qpsolver = qpsolver;net.qpsize = qpsize;net.alphatol = 1e-2;net.kkttol = 5e-2;net.chunksize = 500;%     'chunksize' = Large matrix operations (for example when evaluating%       the kernel functions) are split up into submatrices with maximum%       size [NET.chunksize, NET.chunksize]. Default value: 500net.recompute = Inf;%     'recompute' = During training, the SVM outputs are updated%       iteratively. After NET.recompute iterations the SVM outputs are%       built again from scratch. Lower this when high precision is required.