A computer-implemented method for using an image classifier , particularly a binary classifier, for classifying sensor signals of an optical sensor for providing an actuator control signal (A) for controlling an actuator , in which said actuator controls an at least partially autonomous robot and/or a manufacturing machine and/or an access control system , wherein said classifier is trained for classifying input signals (x i ) to optimize performance according to a non-decomposable metric that measures an alignment between classifications (y i ) corresponding to input signals (x i ) of a set of training data and corresponding predicted classifications (y i ) of said input signals obtained from said classifier, the training comprising the steps of: - providing weighting factors ( a j , b j , f j , g j ) that characterize how said non-decomposable metric depends on a plurality of terms (RP, TN, PP, AP, AN) from a confusion matrix of said classifications ( y i ) and said predicted classifications ( ŷ i ); - training said classifier depending on said provided weighting factors ( a j , b j , fj, g j ) , wherein the optimization is carried out as an adversarial prediction method, wherein the method for using the image classifier comprises the steps of: - receiving a sensor signal (S) comprising data from an optical sensor , - determining an input signal (x) which depends on said sensor signal (S), and - feeding said input signal (x) into said classifier to obtain an output signal (y) that characterizes a classification of said input signal (x), - determining said actuator control signal (A) depending on said output signal (y). in which said actuator controls an at least partially autonomous robot and/or a manufacturing machine and/or an access control system , wherein said classifier is trained for classifying input signals (x i ) to optimize performance according to a non-decomposable metric that measures an alignment between classifications (y i ) corresponding to input signals (x i ) of a set of training data and corresponding predicted classifications (y i ) of said input signals obtained from said classifier, the training comprising the steps of: - providing weighting factors ( a j , b j , f j , g j ) that characterize how said non-decomposable metric depends on a plurality of terms (RP, TN, PP, AP, AN) from a confusion matrix of said classifications ( y i ) and said predicted classifications ( ŷ i ); - training said classifier depending on said provided weighting factors ( a j , b j , fj, g j ) , - providing weighting factors ( a j , b j , f j , g j ) that characterize how said non-decomposable metric depends on a plurality of terms (RP, TN, PP, AP, AN) from a confusion matrix of said classifications ( y i ) and said predicted classifications ( ŷ i ); - training said classifier depending on said provided weighting factors ( a j , b j , fj, g j ) , wherein the optimization is carried out as an adversarial prediction method, wherein the method for using the image classifier comprises the steps of: - receiving a sensor signal (S) comprising data from an optical sensor , - determining an input signal (x) which depends on said sensor signal (S), and - feeding said input signal (x) into said classifier to obtain an output signal (y) that characterizes a classification of said input signal (x), - determining said actuator control signal (A) depending on said output signal (y). - receiving a sensor signal (S) comprising data from an optical sensor , - determining an input signal (x) which depends on said sensor signal (S), and - feeding said input signal (x) into said classifier to obtain an output signal (y) that characterizes a classification of said input signal (x), - determining said actuator control signal (A) depending on said output signal (y). <EOS>