ART

Question 1
Marks : +2 | -2
Pass Ratio : 100%
What is the purpose of ART?
take care of approximation in a network
take care of update of weights
take care of pattern storage
none of the mentioned
Explanation:
Adaptive resonance theory take care of stability plasticity dilemma.
Question 2
Marks : +2 | -2
Pass Ratio : 100%
What does vigilance parameter in ART determines?
number of possible outputs
number of desired outputs
number of acceptable inputs
none of the mentioned
Explanation:
Vigilance parameter in ART determines the tolerance of matching process.
Question 3
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An auto – associative network is?
network in neural which contains feedback
network in neural which contains loops
network in neural which no loops
none of the mentioned
Explanation:
An auto – associative network contains feedback.
Question 4
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hat type learning is involved in ART?
supervised
unsupervised
supervised and unsupervised
none of the mentioned
Explanation:
CPN is a unsupervised learning.
Question 5
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ART is made to tackle?
stability problem
hard problems
storage problems
none of the mentioned
Explanation:
ART is made to tackle stability – plasticity dilemma.
Question 6
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What type of inputs does ART – 1 receives?
bipolar
binary
both bipolar and binary
none of the mentiobned
Explanation:
ART – 1 receives only binary inputs.
Question 7
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The bidirectional associative memory is similar in principle to?
hebb learning model
boltzman model
Papert model
none of the mentioned
Explanation:
The bidirectional associative memory is similar in principle to Hopfield model.
Question 8
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What does ART stand for?
Automatic resonance theory
Artificial resonance theory
Adaptive resonance theory
None of the mentioned
Explanation:
ART stand for Adaptive resonance theory.
Question 9
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Pass Ratio : 100%
What is true about sigmoidal neurons?
can accept any vectors of real numbers as input
outputs a real number between 0 and 1
they are the most common type of neurons
all of the mentioned
Explanation:
These all statements itself defines sigmoidal neurons.
Question 10
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Pass Ratio : 100%
A greater value of ‘p’ the vigilance parameter leads to?
small clusters
bigger clusters
no change
none of the mentioned
Explanation:
Input samples associated with same neuron get reduced.