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<h1>WeakLearn_train</h1>
<introduction>
<p>Implements the weak learner in the RankBoost paper.</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>[i_star,theta_star,qdef_star,r_star]=WeakLearn_train(data,D)</pre><h2>Description<a name="2"></a></h2><pre class="codeinput"><span class="comment">%Y. Freund, R. Iyer, and R. Schapire, ÿAn efficient boosting algorithm for combining preferences,ÿ Journal of Machine Learning Research, vol. 4,pp. 933ÿ969, 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>D ... distribution over the example pairs</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>i_star ... the best feature</li>
<li>theta_star ... the best threshold</li>
<li>qdef_star ... the best default score</li>
<li>r_star ... the maximum value of r</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 7, 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><pre class="codeinput"><span class="comment">%--------------------------------------------------------------------------</span>
<span class="comment">% Precompute the potential</span>
<span class="comment">%--------------------------------------------------------------------------</span>
</pre><p class="footer"><br>
Published with wg_publish; V1.0<br></p>
</div>
<!--
##### SOURCE BEGIN #####
%% WeakLearn_train
% Implements the weak learner in the RankBoost paper.
%% Syntax
% [i_star,theta_star,qdef_star,r_star]=WeakLearn_train(data,D)
%% 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]
% * D ... distribution over the example pairs
%%
% 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
%%
%%
% * i_star ... the best feature
% * theta_star ... the best threshold
% * qdef_star ... the best default score
% * r_star ... the maximum value of r
%%
%% Signature
%%
%%
% * *Author:* Vikas Chandrakant Raykar
% * *E-Mail:* vikas@cs.umd.edu
% * *Date:* October 7, 2006
%%
%% See also
%%
% convert_data_to_ranking_format
%%
%%
%REPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASH
% Precompute the potential
%REPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASH
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