% 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));