This set of Neural Networks Multiple Choice Questions & Answers (MCQs) focuses on “Introduction″.
1. Why do we need biological neural networks?
a) to solve tasks like machine vision & natural language processing
b) to apply heuristic search methods to find solutions of problem
c) to make smart human interactive & user friendly system
d) all of the mentioned
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Explanation: These are the basic aims that a neural network achieve.
2. What is the trend in software nowadays?
a) to bring computer more & more closer to user
b) to solve complex problems
c) to be task specific
d) to be versatile
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Explanation: Software should be more interactive to the user, so that it can understand its problem in a better fashion.
3. What’s the main point of difference between human & machine intelligence?
a) human perceive everything as a pattern while machine perceive it merely as data
b) human have emotions
c) human have more IQ & intellect
d) human have sense organs
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Explanation: Humans have emotions & thus form different patterns on that basis, while a machine(say computer) is dumb & everything is just a data for him.
4. What is auto-association task in neural networks?
a) find relation between 2 consecutive inputs
b) related to storage & recall task
c) predicting the future inputs
d) none of the mentioned
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Explanation: This is the basic definition of auto-association in neural networks.
5. Does pattern classification belongs to category of non-supervised learning?
a) yes
b) no
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Explanation: Pattern classification belongs to category of supervised learning.
6. In pattern mapping problem in neural nets, is there any kind of generalization involved between input & output?
a) yes
b) no
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Explanation: The desired output is mapped closest to the ideal output & hence there is generalisation involved.
7. What is unsupervised learning?
a) features of group explicitly stated
b) number of groups may be known
c) neither feature & nor number of groups is known
d) none of the mentioned
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Explanation: Basic definition of unsupervised learning.
8. Does pattern classification & grouping involve same kind of learning?
a) yes
b) no
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Explanation: Pattern classification involves supervised learning while grouping is an unsupervised one.
9. Does for feature mapping there’s need of supervised learning?
a) yes
b) no
View Answer
Explanation: Feature mapping can be unsupervised, so it’s not a sufficient condition.
10. Example of a unsupervised feature map?
a) text recognition
b) voice recognition
c) image recognition
d) none of the mentioned
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Explanation: Since same vowel may occur in different context & its features vary over overlapping regions of different vowels.
11. What is plasticity in neural networks?
a) input pattern keeps on changing
b) input pattern has become static
c) output pattern keeps on changing
d) output is static
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Explanation: Dynamic nature of input patterns in an AI(Artificial Intelligence) problem.
12. What is stability plasticity dilemma ?
a) system can neither be stable nor plastic
b) static inputs & categorization can’t be handled
c) dynamic inputs & categorization can’t be handled
d) none of the mentioned
View Answer
Explanation: If system is allowed to change its categorization according to inputs it cannot be used for patterns classification & assessment.
13. Drawbacks of template matching are?
a) time consuming
b) highly restricted
c) more generalized
d) none of the the mentioned
View Answer
Explanation: Point to point pattern matching is carried out in the process.
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.
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