Prior knowledge for pattern recognition

<p><a href="/facts/Pattern_recognition/o1s38opj">Pattern recognition</a> is a very active field of research intimately bound to <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>. Also known as classification or <a href="/facts/Statistical_classification/jXXHRkXR">statistical classification</a>, pattern recognition aims at building a <a href="/facts/Classifier_(mathematics)/jXXHRkXR">classifier</a> that can determine the class of an input pattern. This procedure, known as training, corresponds to learning an unknown decision function based only on a set of input-output pairs 
  
    
      
        (
        
          
            x
          
          
            i
          
        
        ,
        
          y
          
            i
          
        
        )
      
    
    {\displaystyle ({\boldsymbol {x}}_{i},y_{i})}
  
 that form the training data (or training set). Nonetheless, in real world applications such as <a href="/facts/Character_recognition/kdTyxa1l">character recognition</a>, a certain amount of information on the problem is usually known beforehand. The incorporation of this prior knowledge into the training is the key element that will allow an increase of performance in many applications.
</p>

Prior knowledge for pattern recognition open-in-new

Prior knowledge for pattern recognition