SPIDER The Spider Objects

Splitting Perceptron

   Generates a Splitting Perceptron (SP) object with given hyperparameters.
   The splitting perceptron algorithm tries to separate the first r
   (best) candidates from the last k (worst) of a given set of
   candidates for a (not specified) example with a discriminative function 
   f(x) = . 
   The set of training candidates is assumed to be sorted in non-decreasing order 
   accoring to their quality.
   As the number of candidates for each example might be large, the training
   data is given as follows: 
   X stores m times the base name (including path) of the files containing
   the candidates. The actual files have a number which conincides with
   the label Y. E.g. if X(i,:) = '../data/candidate', the corresponding
   file would be ['../data/candidate' num2str(Y(i)) '.mat']. The name of the
   variable in the file holding the feature vectors of the
   candidates as row vectors is assumed to be "X".
   ATTENTION: You have to give a name to your datasets as the first
   parameter is interpreted as a name due to spider specification. If you do
   not name your dataset spider will interpret the filenames as name of the
   Hyperparameters (with defaults)
   r=1                  -- rank to which the candidates are considered
   k=1                  -- number of last ranks for which the candidates
                             are assumed to be worst (i.e. the last n-k candidates)
   tau=1                -- margin by which the candidates shall be
   loops=100            -- maximal number of iterations of training
    alpha               -- the weights
    train, test
     Say you have 100 files with candidates in ../demos/data/toy_candidate with 
     core name "candidate"
   X = repmat(['../demos/data/toy_candidate/candidate'],100,1);
   d = data('training',X,[1:100]')
   a = splitting_perceptron
   a.r = 30; a.k = 30; a.tau = 20;
   [r a]=train(a,d);

Reference : Discriminative Reranking for Machine Translation
Author : Libin Shen, Anoop Sarkar and Franz Josef Och
Link : http://www.sfu.ca/~anoop/papers/pdf/drmt.pdf