Systems and methods that comprise receiving a training set of samples, a set of features for a first subset of the set of features, a feature value with a first subset of the training set, a weight with a second subset of the training set. Determining for a second subset of the set of features, a first threshold value with a first metric minimized, and determining, for a third subset of the set of features, a second threshold value with a second metric is minimized, then determining, for a fourth subset of the set of features, a number of thresholds, determining, for a fifth subset of the set of features, an error value based on the number of thresholds, afterward determining the feature having the lowest error, and finally updating the weights by classifying a sample as either belonging to an object class or not based on the strong classifier.