This set of Neural Networks Multiple Choice Questions & Answers (MCQs) focuses on “Analysis of Feature Mapping Network″.
1. What kind of learning is involved in pattern clustering task?
a) supervised
b) unsupervised
c) learning with critic
d) none of the mentioned
View Answer
Explanation: Since pattern classes are formed on unlabelled classes.
2. In pattern clustering, does physical location of a unit relative to other unit has any significance?
a) yes
b) no
c) depends on type of clustering
d) none of the mentioned
View Answer
Explanation: Physical location of a unit doesn’t effect the output.
3. How is feature mapping network distinct from competitive learning network?
a) geometrical arrangement
b) significance attached to neighbouring units
c) nonlinear units
d) none of the mentioned
View Answer
Explanation: Both the geometrical arrangement and significance attached to neighbouring units make it distinct.
4. What is the objective of feature maps?
a) to capture the features in space of input patterns
b) to capture just the input patterns
c) update weights
d) to capture output patterns
View Answer
Explanation: The objective of feature maps is to capture the features in space of input patterns.
5. How are weights updated in feature maps?
a) updated for winning unit only
b) updated for neighbours of winner only
c) updated for winning unit and its neighbours
d) none of the mentioned
View Answer
Explanation: Weights are updated in feature maps for winning unit and its neighbours.
6. In feature maps, when weights are updated for winning unit and its neighbour, which type learning it is known as?
a) karnaugt learning
b) boltzman learning
c) kohonen’s learning
d) none of the mentioned
View Answer
Explanation: Self organization network is also known as Kohonen learning.
7. In self organizing network, how is layer connected to output layer?
a) some are connected
b) all are one to one connected
c) each input unit is connected to each output unit
d) none of the mentioned
View Answer
Explanation: In self organizing network, each input unit is connected to each output unit.
8. What is true regarding adaline learning algorithm
a) uses gradient descent to determine the weight vector that leads to minimal error
b) error is defined as MSE between neurons net input and its desired output
c) this technique allows incremental learning
d) all of the mentioned
View Answer
Explanation: Incremental learning means refining of the weights as more training samples are added, rest are basic statements that defines adaline learning.
9. What is true for competitive learning?
a) nodes compete for inputs
b) process leads to most efficient neural representation of input space
c) typical for unsupervised learning
d) all of the mentioned
View Answer
Explanation: These all statements defines the competitive learning.
10. Use of nonlinear units in the feedback layer of competitive network leads to concept of?
a) feature mapping
b) pattern storage
c) pattern classification
d) none of the mentioned
View Answer
Explanation: Use of nonlinear units in the feedback layer of competitive network leads to concept of pattern clustering.
Sanfoundry Global Education & Learning Series – Neural Networks.
To practice all areas of Neural Networks, here is complete set on 1000+ Multiple Choice Questions and Answers.
