  The notion of incremental learning is to train an ANN algorithm in stages, as
and when newer training data arrives. Incremental learning is becoming
widespread in recent times with the advent of deep learning. Noise in the
training data reduces the accuracy of the algorithm. In this paper, we make an
empirical study of the effect of noise in the training phase. We numerically
show that the accuracy of the algorithm is dependent more on the location of
the error than the percentage of error. Using Perceptron, Feed Forward Neural
Network and Radial Basis Function Neural Network, we show that for the same
percentage of error, the accuracy of the algorithm significantly varies with
the location of error. Furthermore, our results show that the dependence of the
accuracy with the location of error is independent of the algorithm. However,
the slope of the degradation curve decreases with more sophisticated algorithms

Machine Learning;Machine Learning