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Artificial Intelligence - Quiz(MCQ)
A)
Backtracking algorithm
B)
Backpropagation algorithm
C)
Feed-forward neural network
D)
Feed Forward-backward algorithm

Correct Answer : Option (C) :   Feed-forward neural network


Explanation : A perceptron is a Feed-forward neural network with no hidden units that can be representing only linear separable functions. If the data are linearly separable, a simple weight updated rule can be used to fit the data exactly.

A)
Inductive learning
B)
Weak learning
C)
Forced based learning
D)
Unsupervised Learning

Correct Answer : Option (A) :   Inductive learning


Explanation : Computational learning theory analyzes the sample complexity and computational complexity of inductive learning. There is a tradeoff between the expressiveness of the hypothesis language and the ease of learning.

A)
Nonlinear Functions
B)
Linear Functions
C)
Discrete Functions
D)
Exponential Functions

Correct Answer : Option (A) :   Nonlinear Functions


Explanation : Neural networks parameters can be learned from noisy data and they have been used for thousands of applications, so it varies from problem to problem and thus use nonlinear functions.

A)
Regular Hypothesis
B)
Consistent Hypothesis
C)
Irregular Hypothesis
D)
Inconsistent Hypothesis

Correct Answer : Option (B) :   Consistent Hypothesis


Explanation : Inductive learning involves finding a consistent hypothesis that agrees with examples. The difficulty of the task depends on the chosen representation.

A)
an auto-associative neural network
B)
a neural network that contains feedback
C)
a double layer auto-associative neural network
D)
a single layer feed-forward neural network with pre-processing

Correct Answer : Option (D) :   a single layer feed-forward neural network with pre-processing


Explanation : The perceptron is a single layer feed-forward neural network. It is not an auto-associative network because it has no feedback and is not a multiple layer neural network because the pre-processing stage is not made of neurons.

A)
a neural network that has only one loop
B)
a neural network that contains no loops
C)
a neural network that contains feedback
D)
a single layer feed-forward neural network with pre-processing

Correct Answer : Option (C) :   a neural network that contains feedback


Explanation : An auto-associative network is equivalent to a neural network that contains feedback. The number of feedback paths(loops) does not have to be one.

A)
It has set of nodes and connections
B)
Each node computes it’s weighted input
C)
Node could be in excited state or non-excited state
D)
All of the above

Correct Answer : Option (D) :   All of the above

A)
It is powerful and easy neural network
B)
It is software used by Neurosurgeon
C)
A software used to analyze neurons
D)
Designed to aid experts in real world

Correct Answer : Option (A) :   It is powerful and easy neural network

A)
Because it can be solved by a single layer perceptron
B)
Because it is the simplest linearly inseparable problem that exists.
C)
Because it can be expressed in a way that allows you to use a neural network
D)
Because it is complex binary operation that cannot be solved using neural networks

Correct Answer : Option (B) :   Because it is the simplest linearly inseparable problem that exists.

A)
It can handle noise
B)
It can explain result
C)
It has inherent parallelism
D)
It can survive the failure of some nodes

Correct Answer : Option (B) :   It can explain result


Explanation : The artificial Neural Network (ANN) cannot explain result.