% PRDATASETS: Pattern Recognition Datasets in PRTools format
% Version 2.03 23-Aug-2013
%
%Feature based labeled datasets
%------------------------------
%name      objects  feats  classes
%x80          45      8       3    radial distances of characters
%arrhythmia  420    278       2    presence or absence of cardia arrhythmia
%auto_mpg 	 398      6       2    car/miles-per-gallon
%malaysia    291      8      20    segment features in utility symbols
%biomed      194      5       2    various patient indicators
%breast      683      9       2    Wisconsion breast cancer dataset
%cbands    12000     30      24    chromosome banding patterns
%chromo     1143      8      24    chromosome blob features
%diabetes    768      8       2    Pima Indians Diabetes Database
%ecoli       272      7       3    protein localisation sites
%glass       214      9       4    glass types from chemical components
%heart       297     13       2    heart disease dataset
%imox        192      8       4    radial distances of characters
%ionosphere  351     34       2    radar data
%iris        150      4       3    Fisher's Iris dataset
%liver       345      6       2    liver disorder
%satellite  6435     36       6    spectral data
%sonar       208     60       2    rock / metal sonar features
%soybean1    266     35      19    large Soybeans
%soybean2    136     35       4    small Soybeans
%twonorm    7400     20       2    Leo Breiman's two normal example.
%ringnorm   7400     20       2    Leo Breiman's ringnorm example.
%wine        178     13       3    wine recognition
%mfeat_fac  2000    216      10    face features in digits dataset
%mfeat_fou  2000     76      10    Fourier features in digits dataset
%mfeat_kar  2000     64      10    Karhunen Loeve features in digits dataset
%mfeat_pix  2000    240      10    pixel features in digits dataset
%mfeat_zer  2000     53      10    Zernike moments in digits dataset
%mfeat_mor  2000      6      10    morphological features in digits dataset
%
%Multi-band images (pixels are objects, bands are features)
%----------------------------------------------------------
%name      pixels  bands  classes
%emim      128*128    8      1  A seto of 5 8-band EM images
%lena      256*256    3      1  full-color image
%texturel  5*128*128  7      5  texture features for 5 different texture images
%texturet  256*256    7      5  composite texture image
%
%Image datasets (pixels are features, images are objects)
%--------------------------------------------------------
%name   images  pixels  classes
%kimia     216   64*64    18  resampled Kimia dataset of silhouettes
%mnist8  70000    8*8     10  Normalized MNIST digits
%nist16   2000   16*16    10  Normalized NIST digits