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所属分类:模式识别(视觉/语音等)
开发工具:matlab
文件大小:4251KB
下载次数:51
上传日期:2011-09-27 09:29:26
上 传 者bluewindz
说明:  指纹的提取及处理,是我看到最完整的指纹识别程序,特别给力啊,还有指纹部分库。。超级棒
(Fingerprint extraction and processing, is that I see most complete fingerprint identification procedures, in particular to force ah, there is the fingerprint part of the library. . Super Stick)

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1\101_1.tif (90512, 2000-08-25)
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----------------------------------------------- --- Document for MATLAB interface of LIBSVM --- ----------------------------------------------- Introduction ============ This tool provides a simple interface to LIBSVM, a library for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). It is very easy to use as the usage and the way of specifying parameters is the same as that of LIBSVM. Installation ============ On Unix systems, we recommend using GNU g++ as your compiler and type 'make' to build 'svmtrain.mexglx' and 'svmpredict.mexglx'. Note that we assume your MATLAB is installed in '/usr/local/matlab', if not, please change MATLABDIR in Makefile. Example: linux> make On Windows systems, pre-built 'svmtrain.dll' and 'svmpredict.dll' are included in this package, so no need to conduct installation. If you have modified the sources and would like to re-build the package, type 'mex -setup' in MATLAB to choose a compiler for mex first. Then type 'make' to start the installation. Example: matlab> mex -setup (ps: MATLAB will show the following messages to setup default compiler.) Please choose your compiler for building external interface (MEX) files: Would you like mex to locate installed compilers [y]/n? y Select a compiler: [1] Microsoft Visual C/C++ version 6.0 in C:\Program Files\Microsoft Visual Studio [0] None Compiler: 1 Please verify your choices: Compiler: Microsoft Visual C/C++ 6.0 Location: C:\Program Files\Microsoft Visual Studio Are these correct?([y]/n): y matlab> make Usage ===== matlab> model = svmtrain(training_label_vector, training_instance_matrix, [,'libsvm_options']); -training_label_vector: An m by 1 vector of training labels. -training_instance_matrix: An m by n matrix of m training instances with n features. It can be dense or sparse. -libsvm_option: A string of training options in the same format as that of LIBSVM. matlab> [predicted_label, accuracy] = svmpredict(testing_label_vector, testing_instance_matrix, model [,'libsvm_option']); -testing_label_vector: An m by 1 vector of prediction labels. If labels of test data are unknown, simply use any random values. -testing_instance_matrix: An m by n matrix of m testing instances with n features. It can be dense or sparse. -model: The output of svmtrain. -libsvm_option: A string of testing options in the same format as that of LIBSVM. Returned Model Structure ======================== The 'svmtrain' function returns a model which can be used for future prediction. It is a structure and is organized as [Parameters, nr_class, totalSV, rho, Label, ProbA, ProbB, nSV, sv_coef, SVs]: -Parameters: parameters -nr_class: number of classes; = 2 for regression/one-class svm -totalSV: total #SV -rho: -b of the decision function(s) wx+b -Label: label of each class; empty for regression/one-class SVM -ProbA: pairwise probability information; empty if -b 0 or in one-class SVM -ProbB: pairwise probability information; empty if -b 0 or in one-class SVM -nSV: number of SVs for each class; empty for regression/one-class SVM -sv_coef: coefficients for SVs in decision functions -SVs: support vectors If you do not use the option '-b 1', ProbA and ProbB are empty matrices. If the '-v' option is specified, cross validation is conducted and the returned model is just a scalar: cross-validation accuracy for classification and mean-squared error for regression. More details about this model can be found in LIBSVM FAQ (http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html) and LIBSVM implementation document (http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf). Result of Prediction ==================== The function 'svmpredict' has two outputs. The first one, predicted_label, is in general a vector of predicted labels. If '-b 1' is specified as an option of 'svmpredict' and the input model possesses probability information, it is a matrix where additional elements in each row are probabilities that the test data is in each class. Note that the order of classes is the same as Label in the model structure. The second output, accuracy, is a vector including accuracy (for classification), mean squared error, and squared correlation coefficient (for regression). Examples ======== matlab> load heart_scale.mat matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 2'); matlab> [predict_label, accuracy] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data For probability estimates, you need '-b 1' for training and testing: matlab> load heart_scale.mat matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 2 -b 1'); matlab> load heart_scale.mat matlab> [predict_label, accuracy] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1'); Other Utilities =============== A simple matlab program read_sparse.m reads files in libsvm format: [svm_lbl, svm_data] = read_sparse(fname); Two outputs are labels and instances, which can then be used as inputs of svmtrain or svmpredict. This code was initiated by Hsuan-Tien Lin from Caltech and rewritten by Rong-En Fan from National Taiwan University. Additional Information ====================== This interface was initially written by Jun-Cheng Chen, Kuan-Jen Peng, Chih-Yuan Yang and Chih-Huai Cheng from Department of Computer Science, National Taiwan University. The current version was prepared by Rong-En Fan. If you find this tool useful, please cite LIBSVM as follows Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm For any question, please contact Chih-Jen Lin .

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