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Neural Networks Multiple Choice Questions | MCQs | Quiz

Neural Networks Interview Questions and Answers
Practice Neural Networks questions and answers for interviews, campus placements, online tests, aptitude tests, quizzes and competitive exams.

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•   Neural Network Introduction
•   Neural Characteristics - 1
•   Neural Characteristics - 2
•   Neural Characteristics - 3
•   Neural Networks History
•   Neural Terminology
•   Neural Networks Models - 1
•   Neural Networks Models - 2
•   Neural Networks Topology
•   Neural Network Learning - 1
•   Neural Network Learning - 2
•   Neural Network Dynamics
•   Activation Models
•   Learning Basics - 1
•   Learning Basics - 2
•   Learning Laws - 1
•   Learning Laws - 2
•   Stability & Convergence
•   Recall
•   Pattern Association - 1
•   Pattern Association - 2
•   Weight Determination
•   Pattern Classification - 1
•   Pattern Classification - 2
•   Pattern Mapping
•   Pattern Recognition
•   Backpropagation Algorithm
•   Pattern Storage Analysis
•   Feedback Neural Network
•   FF Network Analysis
•   Pattern Storage Networks-1
•   Pattern Storage Networks-2
•   Hopfield Model - 1
•   Hopfield Model - 2
•   Stochastic Networks
•   Boltzman Machine - 1
•   Boltzman Machine - 2
•   Learning Neural Network
•   Feedback Layer
•   Feature Mapping Network
•   Associative Memories
•   Multi Layer Neural Network
•   ART
•   Neural Applications - 1
•   Neural Applications - 2

Best Reference Books

Neural Networks Books

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Neural Networks Question and Answers – Pattern Classification – 2

Posted on June 16, 2017 by Manish

This set of Neural Networks Test focuses on “Pattern Classification – 2”.

1. Convergence in perceptron learning takes place if and only if:
a) a minimal error condition is satisfied
b) actual output is close to desired output
c) classes are linearly separable
d) all of the mentioned
View Answer

Answer: c
Explanation: Linear separability of classes is the condition for convergence of weighs in perceprton learning.
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2. When line joining any two points in the set lies entirely in region enclosed by the set in M-dimensional space , then the set is known as?
a) convex set
b) concave set
c) may be concave or convex
d) none of the mentioned
View Answer

Answer: a
Explanation: A convex set is a set of points in M-dimensional space such that line joining any two points in the set lies entirely in region enclosed by the set.

3. Is it true that percentage of linearly separable functions will increase rapidly as dimension of input pattern space is increased?
a) yes
b) no
View Answer

Answer: b
Explanation: There is decrease in number of linearly separable functions as dimension of input pattern space is increased.

4. If pattern classes are linearly separable then hypersurfaces reduces to straight lines?
a) yes
b) no
View Answer

Answer: a
Explanation: Hypersurfaces reduces to straight lines, if pattern classes are linearly separable.

5. As dimensionality of input vector increases, what happens to linear separability?
a) increases
b) decreases
c) no effect
d) doesn’t depend on dimensionality
View Answer

Answer: b
Explanation: Linear separability decreases as dimensionality increases.
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6. In a three layer network, shape of dividing surface is determined by?
a) number of units in second layer
b) number of units in third layer
c) number of units in second and third layer
d) none of the mentioned
View Answer

Answer: a
Explanation: Practically, number of units in second layer determines shape of dividing surface.

7. In a three layer network, number of classes is determined by?
a) number of units in second layer
b) number of units in third layer
c) number of units in second and third layer
d) none of the mentioned
View Answer

Answer: b
Explanation: Practically, number of units in third layer determines number of classes.

8. Intersection of linear hyperplanes in three layer network can only produce convex surfaces, is the statement true?
a) yes
b) no
View Answer

Answer: a
Explanation: Intersection of linear hyperplanes in three layer network can only produce convex surfaces.

9. Intersection of convex regions in three layer network can only produce convex surfaces, is the statement true?
a) yes
b) no
View Answer

Answer: b
Explanation: Intersection of convex regions in three layer network can produce nonconvex regions.

10. If the output produces nonconvex regions, then how many layered neural is required at minimum?
a) 2
b) 3
c) 4
d) 5
View Answer

Answer: c
Explanation: Adding one more layer of units to three layer can yield surfaces which can separate even nonconvex regions.
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Sanfoundry Global Education & Learning Series – Neural Networks.

To practice all areas of Neural Networks for various tests, here is complete set on 1000+ Multiple Choice Questions and Answers.

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» Next Page - Neural Networks Question and Answers – Pattern Mapping

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Manish Bhojasia
Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer & SAN Architect and is passionate about competency developments in these areas. He lives in Bangalore and delivers focused training sessions to IT professionals in Linux Kernel, Linux Debugging, Linux Device Drivers, Linux Networking, Linux Storage, Advanced C Programming, SAN Storage Technologies, SCSI Internals & Storage Protocols such as iSCSI & Fiber Channel. Stay connected with him @ LinkedIn | Facebook | Twitter

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