文件列表:

ImportanceClassifier.m (314, 2017-06-08)
MKBoostS2Test.m (1662, 2017-06-08)
MKBoostS2Train.m (3396, 2017-06-08)
MKBoostS2cla.m (4453, 2017-06-08)
WeightError.m (140, 2017-06-08)
demo.mat (3246545, 2017-06-08)
libsvm-3.21 (0, 2017-06-08)
libsvm-3.21\COPYRIGHT (1497, 2017-06-08)
libsvm-3.21\FAQ.html (83087, 2017-06-08)
libsvm-3.21\Makefile (732, 2017-06-08)
libsvm-3.21\Makefile.win (1136, 2017-06-08)
libsvm-3.21\heart_scale (27670, 2017-06-08)
libsvm-3.21\java (0, 2017-06-08)
libsvm-3.21\java\Makefile (624, 2017-06-08)
libsvm-3.21\java\libsvm.jar (51947, 2017-06-08)
libsvm-3.21\java\libsvm (0, 2017-06-08)
libsvm-3.21\java\libsvm\svm.java (63839, 2017-06-08)
libsvm-3.21\java\libsvm\svm.m4 (63132, 2017-06-08)
libsvm-3.21\java\libsvm\svm_model.java (868, 2017-06-08)
libsvm-3.21\java\libsvm\svm_node.java (115, 2017-06-08)
libsvm-3.21\java\libsvm\svm_parameter.java (1288, 2017-06-08)
libsvm-3.21\java\libsvm\svm_print_interface.java (87, 2017-06-08)
libsvm-3.21\java\libsvm\svm_problem.java (136, 2017-06-08)
libsvm-3.21\java\svm_predict.java (4950, 2017-06-08)
libsvm-3.21\java\svm_scale.java (8944, 2017-06-08)
libsvm-3.21\java\svm_toy.java (12269, 2017-06-08)
libsvm-3.21\java\svm_train.java (8355, 2017-06-08)
libsvm-3.21\java\test_applet.html (81, 2017-06-08)
libsvm-3.21\matlab (0, 2017-06-08)
libsvm-3.21\matlab\Makefile (1240, 2017-06-08)
libsvm-3.21\matlab\libsvm.dll (255488, 2017-06-08)
libsvm-3.21\matlab\libsvmread.c (4063, 2017-06-08)
libsvm-3.21\matlab\libsvmread.mexw64 (13824, 2017-06-08)
libsvm-3.21\matlab\libsvmwrite.c (2341, 2017-06-08)
libsvm-3.21\matlab\libsvmwrite.mexw64 (12800, 2017-06-08)
... ...

License ========= Copyright (C) 2017 Jianxin Wang(jxwang@mail.csu.edu.cn),Jin Liu(liujin06@csu.edu.cn) This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, see . Jianxin Wang(jxwang@mail.csu.edu.cn),Jin Liu(liujin06@csu.edu.cn) School of Information Science and Engineering Central South University ChangSha CHINA, 410083 Using a two-step multiple kernel learning method to perform Alzheimer’s disease classification. ================= The method is used to improve Alzheimer’s disease classification by combining multiple measures. This method contains two steps: Step 1. Running MKBoostS2cla file to obtain the best classification accuracy for each feature set. Code: MKBoostS2cla.m Input: Ktrain - training data kernel matrix of dimension M * M * N (M is the number of training subjects, and N is the number of base kernels); label_train - label of training data; Ktest - test data kernel matrix of dimension Mtesting * M * N (Mtesting is the number of testing subjects); label_test - label of test data. Output: The best accuracy and the correponding prediect label for each feature set. Step 2: Running wMKL file to further combine multiple feature sets. Code: wMKL.m Input: Ktrain - training data kernel matrix of dimension M * M * N ; label_train - label of training data; Ktest - test data kernel matrix of dimension Mtesting * M * N ; label_test - label of test data; The best accuracy and the correponding prediect label for each feature set. Output: Accuracy, sensitivity and specificity using node feature sets; Accuracy, sensitivity and specificity using edge feature sets; Accuracy, sensitivity and specificity using node and edge feature sets. Before running this method, please install LIBSVM library in MATLAB. In addition, all files of Dataset and Code should be stored in the same folder to run this method.

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