% BINARY LOGISTIC REGRESSION ESTIMATION USING A QUADRATIC PENALTY.  Returns 
% the maximum penalized likelihood estimate for the parameters of a logistic
% regression model.  The inputs in the training cases are in X, which should 
% be a matrix with one row per training case and one column per input variable.
% The training targets are in y, which should be a vector of 0/1 values.  The 
% first element of the parameter vector returned is the intercept; the 
% remaining elements are the coefficients of the inputs.  

function est = lrest (y, X, lambda)

  est = fminunc (@(beta) - lrloglike(y,X,beta), zeros(1,size(X,2)+1));