150. This neural network explanation technique is used to determine the relative importance of individual input attributes.
A. sensitivity analysis
B. average member technique
C. mean squared error analysis
D. absolute average technique

ANSWER: A

151. Which one of the following is not a major strength of the neural network approach?
A. Neural networks work well with datasets comprising noisy data.
B. Neural networks can be used for both supervised learning and unsupervised clustering.
C. Neural network learning algorithms are guaranteed to converge to an optimal solution.
D. None of the above

ANSWER: C

152. During back propagation training, the use of the delta rule is to make weight adjustments so as to
A. Minimize the number of times …show more content…
Epochs represent the total number of
A. Input layer nodes.
B. Passes of the training data through the network.
C. Network nodes.
D. passes of the test data through the network

ANSWER: B
154. Two classes each of which is represented by the same pair of numeric attributes are linearly separable if
A. at least one of the pairs of attributes shows a curvilinear relationship between the classes.
B. at least one of the pairs of attributes shows a high positive correlation between the classes.
C. at least one of the pairs of attributes shows a high positive correlation between the classes.
D. a straight line partitions the instances of the two classes

ANSWER: D

155. The test set accuracy of a back propagation neural network can often be improved by
A. Increasing the number of epochs used to train the network.
B. Decreasing the number of hidden layer nodes.
C. Increasing the learning rate.
D. None of the above

ANSWER: A

156. Which one is the type of supervised network design does not contain a hidden layer.
A. back propagation
B. perceptron
C. self-organizing map
D. genetic

ANSWER: B

157. The total delta measures the total absolute change in network connection weights for each pass of the training data through a neural network. This value is most often used to determine the convergence of