Machine Learning - DISCo

110 MACHINE LEARNING
Error versus weight updates (example 1)
0.008
0.007
Validation set error
0 5000 loo00 15000 20000
Number of weight updates
Error versus weight updates (example 2)
0.08 %** I r 8
0.07 - Training set error * -
Validation set error +
0.06
y+:L
0 lo00 2000 3000 4000 5000 6000
Number of weight updates
FIGURE 4.9
Plots of error E as a function of the number of weight updates, for two different robot perception
tasks. In both learning cases, error E over the training examples decreases monotonically, as gradient
descent minimizes this measure of error. Error over the separate "validation" set of examples typically
decreases at first, then may later increase due to overfitting the training examples. The network most
IikeIy to generalize correctly to unseen data is the network with the lowest error over the validation
set. Notice in the second plot, one must be careful to not stop training too soon when the validation
set error begins to increase.
this variation for two fairly typical applications of BACKPROPAGATION. Consider
first the top plot in this figure. The lower of the two lines shows the monotonically
decreasing error E over the training set, as the number of gradient descent
iterations grows. The upper line shows the error E measured over a different validation
set of examples, distinct from the training examples. This line measures the
generalization accuracy of the network-the accuracy with which it fits examples
beyond the training data.