rankboost_ranking_function.m

RankBoost算法实现

function [o]=RankBoost_ranking_function(X,T,model)
% Implementation of the ranking function for the RankBoost
%
%% Input
% * X ... d x N matrix, each column is one input.
% * T ... number of weak learners
% * model ... structure containing all the learnt weak learners
% * model.alpha ... the weight
% * model.i... the best feature
% * model.theta ... the best threshold
% * model.qdef ... the best default score
%
%% Ouput
% * o ... 1 x N vector of the outputs
%
%% Signature
% Author: Vikas Chandrakant Raykar
% E-Mail: [e-mail]
% Date: October 07, 2006
%

[d,N]=size(X);

o=zeros(1,N);

for t=1:T
    o=o+model.alpha(t)*WeakLearn_ranking_function(X,model.i(t),model.theta(t),model.qdef(t));
end

return