A method of online training of a classifier, the method comprising: receiving an object; determining a distance from at least one feature vector of the object, which is received during the online training, to a first, predetermined, decision boundary established during off-line training for the classifier, the first decision boundary defined between a real example vector and a fake example vector; updating , based on one or more objects received during online training as positive examples that match the first decision boundary classified as respective one or more real objects and each having respective real example vectors , a decision rule as a function of at least the distance, the decision rule being a second decision boundary established during the online training wherein updating the decision rule comprises reducing a margin between the at least one feature vector of the object and the second decision boundary; and classifying a future object as a real object based at least in part on the updated decision rule and the first, predetermined, decision boundary, wherein the classifying comprises classifying the future object as a real object if the feature vector distance of the future object to the first, predetermined, decision boundary is greater than (D min - M min ) × (1-1/N) k , wherein D min is the closest distance of the real example vectors received during the online training to the first, predetermined, decision boundary , M min is a minimum margin from the real example vector that is closest to the decision boundary to the first predetermined decision boundary , N is the number of received examples, and k is a fixed parameter that controls the rate at which D min is moved to the real example vector that is closest to the first, predetermined, decision boundary. receiving an object; determining a distance from at least one feature vector of the object, which is received during the online training, to a first, predetermined, decision boundary established during off-line training for the classifier, the first decision boundary defined between a real example vector and a fake example vector; updating , based on one or more objects received during online training as positive examples that match the first decision boundary classified as respective one or more real objects and each having respective real example vectors , a decision rule as a function of at least the distance, the decision rule being a second decision boundary established during the online training wherein updating the decision rule comprises reducing a margin between the at least one feature vector of the object and the second decision boundary; and classifying a future object as a real object based at least in part on the updated decision rule and the first, predetermined, decision boundary, wherein the classifying comprises classifying the future object as a real object if the feature vector distance of the future object to the first, predetermined, decision boundary is greater than (D min - M min ) × (1-1/N) k , wherein D min is the closest distance of the real example vectors received during the online training to the first, predetermined, decision boundary , M min is a minimum margin from the real example vector that is closest to the decision boundary to the first predetermined decision boundary , N is the number of received examples, and k is a fixed parameter that controls the rate at which D min is moved to the real example vector that is closest to the first, predetermined, decision boundary. <EOS>