Transcript Building Neural Networks with iDA

Building Neural Networks with iDA
Chapter 9
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9.1 A Four-Step Approach for
Backpropagation Learning
1.
2.
3.
4.
Prepare the data to be mined.
Define the network architecture.
Watch the network train.
Read and interpret summary
results.
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Input Layer
1.0
Hidden Layer
Node 1
W1j
W1i
Node j
Wjk
W2j
0.4
Output Layer
Node 2
W2i
Node k
Node i
Wik
W3j
0.7
Node 3
W3i
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Example 1: Modeling the
Exclusive-OR Function
Table 9.1 • The Exclusive-OR Function
Input 1
Input 2
XOR
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0
1
0
1
1
0
0
0
1
1
0
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McCulloch-Pitts Neurons
(Perceptron networks)
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1.2
B
A
1
0.8
0.6
Input 2
0.4
0.2
B
A
0
Input 1
0
0.2
0.4
0.6
0.8
1
1.2
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Step 1: Prepare The Data To Be
Mined
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Step 2: Define The Network
Architecture
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Step 3: Watch The Network
Train
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Step 4: Read and Interpret
Summary Results
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Example 2: The Satellite Image
Dataset
Step 1: Prepare The Data To Be
Mined
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Step 2: Define The Network
Architecture
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Step 3: Watch The Network Train
Step 4: Read And Interpret Summary
Results
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General considerations
• Art and science
• Parameter choices: a combination of
creativity and rational reasoning
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9.2 A Four-Step Approach for Neural
Network Clustering
Output Layer
Input Layer
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Node 1
Node 2
Step 1: Prepare The Data To Be
Mined: The Deer Hunter Dataset
Step 2: Define The Network Architecture
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Step 3: Watch The Network
Train
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Step 4: Read And Interpret Summary
Results
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General consideration
• Output grid should be larger than the size
of the final clustering
• Small rms values indicate higher-quality
clusters
• Explanation, interesting patterns
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9.3 ESX for Neural Network
Cluster Analysis
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