Question 1

Neural network training is accomplished by repeatedly passing the training data through the network while

Accepted Answer

A)  individual network weights are modified. 
B)  training instance attribute values are modified. 
C)  the ordering of the training instances is modified. 
D)  individual network nodes have the coefficients on their corresponding functional parameters modified. 
A)  individual network weights are modified. 
B)  training instance attribute values are modified. 
C)  the ordering of the training instances is modified. 
D)  individual network nodes have the coefficients on their corresponding functional parameters modified. A

Question 2

During backpropagation training, the purpose of the delta rule is to make weight adjustments so as to

Accepted Answer

A)  minimize the number of times the training data must pass through the network. 
B)  minimize the number of times the test data must pass through the network. 
C)  minimize the sum of absolute differences between computed and actual outputs. 
D)  minimize the sum of squared error differences between computed and actual output. 
A)  minimize the number of times the training data must pass through the network. 
B)  minimize the number of times the test data must pass through the network. 
C)  minimize the sum of absolute differences between computed and actual outputs. 
D)  minimize the sum of squared error differences between computed and actual output. D

Question 3

Which one of the following is not a major strength of the neural network approach?

Accepted Answer

A)  Neural networks work well with datasets containing noisy data. 
B)  Neural networks can be used for both supervised learning and unsupervised clustering. 
C)  Neural network learning algorithms are guaranteed to converge to an optimal solution. 
D)  Neural networks can be used for applications that require a time element to be included in the data. 
A)  Neural networks work well with datasets containing noisy data. 
B)  Neural networks can be used for both supervised learning and unsupervised clustering. 
C)  Neural network learning algorithms are guaranteed to converge to an optimal solution. 
D)  Neural networks can be used for applications that require a time element to be included in the data. C

Question 4

The values input into a feed-forward neural network

Accepted Answer

A)  may be categorical or numeric. 
B)  must be either all categorical or all numeric but not both. 
C)  must be numeric. 
D)  must be categorical. 
A)  may be categorical or numeric. 
B)  must be either all categorical or all numeric but not both. 
C)  must be numeric. 
D)  must be categorical.

Question 5

A two-layered neural network used for unsupervised clustering.

Accepted Answer

A)  backpropagation network 
B)  Kohonen network 
C)  perceptron network 
D)  aggomerative network 
A)  backpropagation network 
B)  Kohonen network 
C)  perceptron network 
D)  aggomerative network

Question 6

Genetic learning can be used to train a feed-forward network. This is accomplished by having each population element represent one possible

Accepted Answer

A)  network configuration of nodes and links. 
B)  set of training data to be fed through the network. 
C)  set of network output values. 
D)  set of network connection weights. 
A)  network configuration of nodes and links. 
B)  set of training data to be fed through the network. 
C)  set of network output values. 
D)  set of network connection weights.

Question 7

A feed-forward neural network is said to be fully connected when

Accepted Answer

A)  all nodes are connected to each other. 
B)  all nodes at the same layer are connected to each other. 
C)  all nodes at one layer are connected to the nodes in the next higher layer. 
D)  all hidden layer nodes are connected to all output layer nodes. 
A)  all nodes are connected to each other. 
B)  all nodes at the same layer are connected to each other. 
C)  all nodes at one layer are connected to the nodes in the next higher layer. 
D)  all hidden layer nodes are connected to all output layer nodes.

Question 8

This neural network explanation technique is used to determine the relative importance of individual input attributes.

Accepted Answer

A)  sensitivity analysis 
B)  average member technique 
C)  mean squared error analysis 
D)  absolute average technique 
A)  sensitivity analysis 
B)  average member technique 
C)  mean squared error analysis 
D)  absolute average technique

Question 9

With a Kohonen network, the output layer node that wins an input instance is rewarded by having

Accepted Answer

A)  a higher probability of winning the next training instance to be presented. 
B)  its connect weights modified to more closely match those of the input instance. 
C)  its connection weights modified to more closey match those of its neighbors. 
D)  neighoring connection weights modified to become less similar to its own connection weights. 
A)  a higher probability of winning the next training instance to be presented. 
B)  its connect weights modified to more closely match those of the input instance. 
C)  its connection weights modified to more closey match those of its neighbors. 
D)  neighoring connection weights modified to become less similar to its own connection weights.

Question 10

Epochs represent the total number of

Accepted Answer

A)  input layer nodes. 
B)  passes of the training data through the network. 
C)  network nodes. 
D)  passes of the test data through the network. 
A)  input layer nodes. 
B)  passes of the training data through the network. 
C)  network nodes. 
D)  passes of the test data through the network.

Neural network training is accomplished by repeatedly passing the training data through the network while

During backpropagation training, the purpose of the delta rule is to make weight adjustments so as to

Which one of the following is not a major strength of the neural network approach?

The values input into a feed-forward neural network

A two-layered neural network used for unsupervised clustering.

Genetic learning can be used to train a feed-forward network. This is accomplished by having each population element represent one possible

A feed-forward neural network is said to be fully connected when

This neural network explanation technique is used to determine the relative importance of individual input attributes.

With a Kohonen network, the output layer node that wins an input instance is rewarded by having

Epochs represent the total number of

Data Mining: a First View

Data Mining: a Closer Look

Basic Data Mining Techniques

An Excel-Based Data Mining Tool

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The Data Warehouse

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Building Neural Networks With Ida

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Exam 8: Neural Networks

Neural network training is accomplished by repeatedly passing the training data through the network while

During backpropagation training, the purpose of the delta rule is to make weight adjustments so as to

Which one of the following is not a major strength of the neural network approach?

The values input into a feed-forward neural network

A two-layered neural network used for unsupervised clustering.

Genetic learning can be used to train a feed-forward network. This is accomplished by having each population element represent one possible

A feed-forward neural network is said to be fully connected when

This neural network explanation technique is used to determine the relative importance of individual input attributes.

With a Kohonen network, the output layer node that wins an input instance is rewarded by having

Epochs represent the total number of

Data Mining: a First View

Data Mining: a Closer Look

Basic Data Mining Techniques

An Excel-Based Data Mining Tool

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Building Neural Networks With Ida

Statistical Techniques

Specialized Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters