Patent ID: 11960991
Assignee: ROBERT BOSCH GMBH
Field: Computer technology (Electrical engineering)
Classification: CPC G  A | IPC A  G

Claim 7:
8. A non-transitory machine readable storage medium on which is stored a computer program for training a binary classifier for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier, the computer program when executed by a computer, causing the computer to perform the following steps:
providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications; and
training the classifier depending on the provided weighting factors;
wherein the non-decomposable metric is given by the formula, Σ
    j
   
   ⁢
   
    
     
      
       a
       j
      
      ·
      TP
     
     +
     
      
       b
       j
      
      ·
      TN
     
     +
     
      
       f
       j
      
      (
      
       PP
       ,
       AP
       ,
       PN
       ,
       AN
      
      )
     
    
    
     
      g
      j
     
     (
     
      PP
      ,
      AP
      ,
      PN
      ,
      AN
     
     )
    
   
  
  ,, where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive″, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative″, AP=″actual positive″ and AN=″actual negative″; and
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint; and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.