文件列表:

classification_toolbox (0, 2016-03-29)
classification_toolbox\classification_toolbox_4.0 (0, 2016-03-29)
classification_toolbox\classification_toolbox_4.0\calc_class_param.m (2495, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\calc_qt_limits.m (1083, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\calc_reg_param.m (907, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\cartcv.m (4627, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\cartfit.m (1632, 2015-10-22)
classification_toolbox\classification_toolbox_4.0\cartpred.m (743, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\class_gui.fig (31313, 2015-10-22)
classification_toolbox\classification_toolbox_4.0\class_gui.m (88652, 2015-11-05)
classification_toolbox\classification_toolbox_4.0\dacompsel.m (2427, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\dacv.m (5710, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\dafit.m (4290, 2015-10-22)
classification_toolbox\classification_toolbox_4.0\dapred.m (2865, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\data_pretreatment.m (1678, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\help (0, 2016-03-29)
classification_toolbox\classification_toolbox_4.0\help.htm (1117, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\help\classparameters.htm (8023, 2015-11-05)
classification_toolbox\classification_toolbox_4.0\help\download.htm (2136, 2015-03-05)
classification_toolbox\classification_toolbox_4.0\help\example.htm (12281, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\help\example_plsda_01.gif (9259, 2013-04-04)
classification_toolbox\classification_toolbox_4.0\help\example_plsda_02.gif (5246, 2013-04-04)
classification_toolbox\classification_toolbox_4.0\help\example_plsda_03.gif (3186, 2013-04-04)
classification_toolbox\classification_toolbox_4.0\help\example_plsda_04.gif (7228, 2013-04-04)
classification_toolbox\classification_toolbox_4.0\help\example_plsda_05.gif (11294, 2013-04-04)
classification_toolbox\classification_toolbox_4.0\help\example_plsda_06.gif (5330, 2013-04-04)
classification_toolbox\classification_toolbox_4.0\help\example_plsda_07.gif (7335, 2013-04-04)
classification_toolbox\classification_toolbox_4.0\help\example_plsda_08.gif (3165, 2013-04-04)
classification_toolbox\classification_toolbox_4.0\help\example_plsda_09.gif (4771, 2013-04-04)
classification_toolbox\classification_toolbox_4.0\help\example_plsda_10.gif (8197, 2013-04-04)
classification_toolbox\classification_toolbox_4.0\help\footer.htm (589, 2013-10-02)
classification_toolbox\classification_toolbox_4.0\help\gui.htm (7498, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\help\gui_1.gif (8295, 2013-02-13)
classification_toolbox\classification_toolbox_4.0\help\gui_2.gif (8013, 2013-02-14)
classification_toolbox\classification_toolbox_4.0\help\gui_3.gif (10892, 2013-02-14)
classification_toolbox\classification_toolbox_4.0\help\gui_5.gif (37173, 2015-11-05)
classification_toolbox\classification_toolbox_4.0\help\gui_calculate.htm (12575, 2015-10-20)
classification_toolbox\classification_toolbox_4.0\help\gui_file.htm (3943, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\help\gui_predict.htm (4613, 2015-10-14)
classification_toolbox\classification_toolbox_4.0\help\gui_results.htm (8959, 2015-10-14)
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-------------------------------------------- Classification toolbox for MATLAB - version 4.0 -------------------------------------------- The Classification toolbox for MATLAB is a collection of MATLAB modules for calculating classification (supervised pattern recognition) multivariate models: Discriminant Analysis, Partial Least Square Discriminant Analysis (PLSDA), Classification trees (CART), K-Nearest Neighbors (kNN), Potential Functions (Kernel Density Estimators), Support Vector Machines (SVM) and Soft Independent Modeling of Class Analogy (SIMCA). This is the version 4.0 of the Classification toolbox for MATLAB Classification toolbox for MATLAB has been released by Milano Chemometrics and QSAR research Group. Visit our website at www.disat.unimib.it/chm -------------------------------------------- INSTALLING THE TOOLBOX -------------------------------------------- MATLAB should be installed, while the Statistics Toolbox is needed to compute some of the classification methods (Discriminant Analysis and CART). In order to install the toolbox, simply copy the files to a folder (e.g. "Classification toolbox for MATLAB"). Then, in order to use it, select the same folder as MATLAB current directory. -------------------------------------------- STARTING CALCULATIONS -------------------------------------------- Before starting calculations, please read the HELP files provided in HTML format. A complete guide on how to calculate models is provided. -------------------------------------------- HELP FILES -------------------------------------------- Help files are provided in HTML format. Open the help.htm file in your favourite browser and read it! -------------------------------------------- CONDITIONS -------------------------------------------- The toolbox is freeware and may be used (but not modified) if proper reference is given to the authors. Preferably refer to the following paper: Ballabio D, Consonni V, (2013) Classification tools in chemistry. Part 1: Linear models. PLS-DA. Analytical methods (2013), 5, 3790-37*** -------------------------------------------- WARRANTY -------------------------------------------- In short, no guarantees, whatsoever, are given for the quality of this toolbox or for the consequences of its use. It is inevitable that there will be some bugs, but we have tried to test the algorithms thoroughly. -------------------------------------------- WEB CONTACTS -------------------------------------------- Download the toolbox or check updates here: http://michem.disat.unimib.it/chm/ Write us for comments, questions or if you find bugs we didn't see! davide.ballabio@unimib.it -------------------------------------------- HAVE FUN!!! --------------------------------------------

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