train_sc

 Train a (statistical) classifier
 
  CC = train_sc(D,classlabel)
  CC = train_sc(D,classlabel,MODE)
  CC = train_sc(D,classlabel, 'REG', W)
    weighting D(k,:) with weight W(k)

 CC contains the model parameters of a classifier which can be applied 
   to test data using test_sc. 
   R = test_sc(CC,D,...) 

  The following classifier types are supported MODE.TYPE
    'MDA'      mahalanobis distance based classifier [1]
    'MD2'      mahalanobis distance based classifier [1]
    'MD3'      mahalanobis distance based classifier [1]
    'GRB'      Gaussian radial basis function     [1]
    'QDA'      quadratic discriminant analysis    [1]
    'LD2'      linear discriminant analysis (see LDBC2) [1]
               MODE.hyperparameter.gamma: regularization parameter [default 0] 
    'LD3'      linear discriminant analysis (see LDBC3) [1]
               MODE.hyperparameter.gamma: regularization parameter [default 0] 
    'LD4'      linear discriminant analysis (see LDBC4) [1]
               MODE.hyperparameter.gamma: regularization parameter [default 0] 
    'LD5'      another LDA (motivated by CSP)
               MODE.hyperparameter.gamma: regularization parameter [default 0] 
    'RDA'      regularized discriminant analysis [7]
               MODE.hyperparameter.gamma: regularization parameter 
               MODE.hyperparameter.lambda =  
        gamma = 0, lambda = 0 : MDA 
        gamma = 0, lambda = 1 : LDA 
         Hint: hyperparameters are used only in test_sc.m, testing different 
        the hyperparameters do not need repetitive calls to train_sc, 
        it is sufficient to modify CC.hyperparameters before calling test_sc.     
    'GDBC'     general distance based classifier  [1]
    ''         statistical classifier, requires Mode argument in TEST_SC    
    '###/GSVD'    GSVD and statistical classifier [2,3], 
    '###/sparse'  sparse  [5] 
               '###' must be 'LDA' or any other classifier 
    'SVM','SVM1r'  support vector machines, one-vs-rest
               MODE.hyperparameter.c_value = 
    'PSVM'    Proximal SVM [8] 
               MODE.hyperparameter.nu  (default: 1.0)
    'PLS'    (linear) partial least squares regression 
    'REG'      regression analysis;
    'WienerHopf'    Wiener-Hopf equation  
    'NBC'    Naive Bayesian Classifier [6]     
    'aNBC'    Augmented Naive Bayesian Classifier [6]
    'NBPW'    Naive Bayesian Parzen Window [9]     
    'SVM11'    support vector machines, one-vs-one + voting
               MODE.hyperparameter.c_value = 
    'RBF'      Support Vector Machines with RBF Kernel
               MODE.hyperparameter.c_value = 
               MODE.hyperparameter.gamma = 
    'LPM'      Linear Programming Machine
               MODE.hyperparameter.c_value = 
    'CSP'    CommonSpatialPattern is very experimental and just a hack
        uses a smoothing window of 50 samples.

  {'MDA','MD2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','REG','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse','RDA','GDBC','SVM','RBF'} 
 
 CC contains the model parameters of a classifier. Some time ago,     
 CC was a statistical classifier containing the mean 
 and the covariance of the data of each class (encoded in the 
  so-called "extended covariance matrices". Nowadays, also other 
 classifiers are supported. 

 see also: TEST_SC, COVM

 References:
 [1] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed.
       John Wiley & Sons, 2001.
 [2] Peg Howland and Haesun Park,
       Generalizing Discriminant Analysis Using the Generalized Singular Value Decomposition
       IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8), 2004.
       dx.doi.org/10.1109/TPAMI.2004.46
 [3] http://www-static.cc.gatech.edu/~kihwan23/face_recog_gsvd.htm
 [4] Jieping Ye, Ravi Janardan, Cheong Hee Park, Haesun Park
       A new optimization criterion for generalized discriminant analysis on undersampled problems.
       The Third IEEE International Conference on Data Mining, Melbourne, Florida, USA
       November 19 - 22, 2003
 [5] J.D. Tebbens and P. Schlesinger (2006),
       Improving Implementation of Linear Discriminant Analysis for the Small Sample Size Problem
    Computational Statistics & Data Analysis, vol 52(1): 423-437, 2007
       http://www.cs.cas.cz/mweb/download/publi/JdtSchl2006.pdf
 [6] H. Zhang, The optimality of Naive Bayes,
     http://www.cs.unb.ca/profs/hzhang/publications/FLAIRS04ZhangH.pdf
 [7] J.H. Friedman. Regularized discriminant analysis.
    Journal of the American Statistical Association, 84:165–175, 1989.
 [8] G. Fung and O.L. Mangasarian, Proximal Support Vector Machine Classifiers, KDD 2001.
        Eds. F. Provost and R. Srikant, Proc. KDD-2001: Knowledge Discovery and Data Mining, August 26-29, 2001, San Francisco, CA.
     p. 77-86.
 [9] Kai Keng Ang, Zhang Yang Chin, Haihong Zhang, Cuntai Guan.
    Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface.
    IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence).
    1-8 June 2008 Page(s):2390 - 2397