svm.zip_SVM_支持向量机SVM

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; end if nargin < 6, qpsolver = ''; end if nargin < 5, use2norm = 0; end if nargin < 4, C = 1; end if nargin < 3, kernelpar = []; end net.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: 500 net.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.