Classification toolbox for MATLAB

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) , Unequal class models (UNEQ), Soft Independent Modeling of Class Analogy (SIMCA), Backpropagation Neural Networks (BPNN). A graphical user interface (GUI), which allows an easy model calculation and analysis of results, is also provided with the toolbox. Help files: HTML files are provided toghter with the MATLAB files in order to help the user. The HTML help provides some underlying information on multivariate classification (see Theory section); it also explains how to prepare your data, how to handle the model settings and how to calculate the classification models. An example of analysis is shown. The toolbox is freeware and may be used if proper reference is given to the authors. Preferably refer to the followign papers: Ballabio D, Consonni V, (2013) Classification tools in chemistry. Part 1: Linear models. PLS-DA. Analytical Methods, 5, 3790-3798 The Classification toolbox for MATLAB is distributed with an Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licenc