📄 rankboost_train.html

📁 RankBoost算法实现
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         <h1>RankBoost_train</h1>
         <introduction>
            <p>RankBoost Training</p>
         </introduction>
         <h2>Contents</h2>
         <div>
            <ul>
               <li><a href="#1">Syntax</a></li>
               <li><a href="#2">Description</a></li>
               <li><a href="#4">Input</a></li>
               <li><a href="#12">Ouput</a></li>
               <li><a href="#14">Signature</a></li>
               <li><a href="#16">See also</a></li>
            </ul>
         </div>
         <h2>Syntax<a name="1"></a></h2><pre>[model,time_taken]=RankBoost_train(data,T,verbose,plot_enable)</pre><h2>Description<a name="2"></a></h2><pre class="codeinput"><span class="comment">%Y. Freund, R. Iyer, and R. Schapire, &yuml;An efficient boosting algorithm for combining preferences,&yuml; Journal of Machine Learning Research, vol. 4,pp. 933&yuml;969, 2003.</span>
</pre><h2>Input<a name="4"></a></h2>
         <div>
            <ul>
               <li>data ... structure containing the data regarding the ranking task at hand [See convert_data_to_ranking_format.m]</li>
               <li>T ... number of boosting rounds</li>
               <li>verbose ... if 1 comments are displayed. Set to 0 when timing the program</li>
               <li>plot_enable ... if 1 plots the learning curve</li>
            </ul>
         </div>
         <p>Basic information---</p>
         <div>
            <ul>
               <li>data.N ... number of data points</li>
               <li>data.d ... data dimensionality</li>
               <li>data.S ... number of classes</li>
               <li>data.m ... number of inputs in each class</li>
            </ul>
         </div>
         <p>Actual data---</p>
         <div>
            <ul>
               <li>data.labels ... vector of class labels</li>
               <li>data.X ... cell array where each cell contains the data belonging to one class</li>
               <li>data.index ... index of the data belonging to one class</li>
               <li>data.X_raw ... d x N original data matrix</li>
               <li>data.y_raw ... 1 x N vector of the class labels</li>
            </ul>
         </div>
         <p>Preference graph---</p>
         <div>
            <ul>
               <li>data.graph_type ... graph type</li>
               <li>data.C ... number of edges in the preference graph</li>
               <li>data.G ... data.C x 2 matrix encoding the preference relations. The class in the second column is preferred over that in the
                  first column.
               </li>
               <li>data.num_of_pairs ... total number of pairwise preference realtions</li>
            </ul>
         </div>
         <h2>Ouput<a name="12"></a></h2>
         <div>
            <ul>
               <li>model ... the final set of weak models learnt</li>
               <li>model.alpha ... the weight</li>
               <li>model.i... the best feature</li>
               <li>model.theta ... the best threshold</li>
               <li>model.qdef ... the best default score</li>
               <li>time_taken ... time taken in seconds</li>
            </ul>
         </div>
         <h2>Signature<a name="14"></a></h2>
         <div>
            <ul>
               <li><b>Author:</b> Vikas Chandrakant Raykar
               </li>
               <li><b>E-Mail:</b> <a href="mailto:vikas@cs.umd.edu">vikas@cs.umd.edu</a></li>
               <li><b>Date:</b> October 07, 2006
               </li>
            </ul>
         </div>
         <h2>See also<a name="16"></a></h2>
         <p><a href="convert_data_to_ranking_format.html">convert_data_to_ranking_format</a></p>
         <p class="footer"><br>
            Published with wg_publish; V1.0<br></p>
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      <!--
##### SOURCE BEGIN #####
%% RankBoost_train
% RankBoost Training
%% Syntax
%  [model,time_taken]=RankBoost_train(data,T,verbose,plot_enable)
%% Description
%%
%Y. Freund, R. Iyer, and R. Schapire, 每An efficient boosting algorithm for combining preferences,每 Journal of Machine Learning Research, vol. 4,pp. 933每969, 2003.
%%
%% Input
%%
%%
% * data ... structure containing the data regarding the ranking task at hand [See convert_data_to_ranking_format.m]
% * T ... number of boosting rounds
% * verbose ... if 1 comments are displayed. Set to 0 when timing the program
% * plot_enable ... if 1 plots the learning curve
%%
% Basic informationREPLACE_WITH_DASH_DASH-
%%
%%
% * data.N ... number of data points
% * data.d ... data dimensionality
% * data.S ... number of classes
% * data.m ... number of inputs in each class
%%
% Actual dataREPLACE_WITH_DASH_DASH-
%%
%%
% * data.labels ... vector of class labels
% * data.X ... cell array where each cell contains the data belonging to one class
% * data.index ... index of the data belonging to one class
% * data.X_raw ... d x N original data matrix
% * data.y_raw ... 1 x N vector of the class labels
%%
% Preference graphREPLACE_WITH_DASH_DASH-
%%
%%
% * data.graph_type ... graph type
% * data.C ... number of edges in the preference graph
% * data.G ... data.C x 2 matrix encoding the preference relations. The class in the second column is preferred over that in the first column.
% * data.num_of_pairs ... total number of pairwise preference realtions
%%
%% Ouput
%%
%%
% * model ... the final set of weak models learnt
% * model.alpha ... the weight
% * model.i... the best feature
% * model.theta ... the best threshold
% * model.qdef ... the best default score
% * time_taken ... time taken in seconds
%%
%% Signature
%%
%%
% * *Author:* Vikas Chandrakant Raykar
% * *E-Mail:* vikas@cs.umd.edu
% * *Date:* October 07, 2006
%%
%% See also
%%
% convert_data_to_ranking_format
%%
%%
%

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