LPBoost

<p>Linear Programming Boosting (LPBoost) is a <a href="/facts/Supervised_learning/JlNFNPFt">supervised</a> <a href="/facts/Statistical_classification/jXXHRkXR">classifier</a> from the <a href="/facts/Boosting_(machine_learning)/HgejTPPu">boosting</a> family of classifiers. LPBoost maximizes a <a href="/facts/Margin_(machine_learning)/DI0QU6QQ">margin</a> between training samples of different classes, and thus also belongs to the class of <a href="/facts/Margin_classifier/vfqL8PqQ">margin classifier</a> algorithms.
</p><p>Consider a classification function 
  
    
      
        f
        :
        
          
            X
          
        
        →
        {
        −
        1
        ,
        1
        }
        ,
      
    
    {\displaystyle f:{\mathcal {X}}\to \{-1,1\},}

which classifies samples from a space 
  
    
      
        
          
            X
          
        
      
    
    {\displaystyle {\mathcal {X}}}
  
 into one of two classes, labelled 1 and -1, respectively.  LPBoost is an algorithm for learning such a classification function, given a <a href="/facts/Training%2c_validation%2c_and_test_data_sets/V3mMQb2G">set of training examples</a> with known class labels. LPBoost is a <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> technique especially suited for joint classification and <a href="/facts/Feature_selection/gxbuF4VG">feature selection</a> in structured domains.
</p>

LPBoost open-in-new

LPBoost