1. Neural Networks Chapter 8

2. 8.1 Feed-Forward Neural Networks

3. Figure 8.1 A fully connected feed- forward neural network

5. Equation 8.1 Neural Network Input Format

6. Neural Network Output Format

7. Equation 8.2 The Sigmoid Function

8. Figure 8.2 The sigmoid function

9. 8.2 Neural Network Training: A Conceptual View

10. Supervised Learning with Feed-Forward Networks Backpropagation Learning Genetic Learning

12. Unsupervised Clustering with Self-Organizing Maps

13. Figure 8.3 A 3x3 Kohonen network with two input layer nodes

14. 8.3 Neural Network Explanation Sensitivity Analysis Average Member Technique

15. 8.4 General Considerations What input attributes will be used to build the network? How will the network output be represented? How many hidden layers should the network contain? How many nodes should there be in each hidden layer? What condition will terminate network training?

16. Neural Network Strengths Work well with noisy data. Can process numeric and categorical data. Appropriate for applications requiring a time element. Have performed well in several domains. Appropriate for supervised learning and unsupervised clustering.

17. Weaknesses Lack explanation capabilities. May not provide optimal solutions to problems. Overtraining can be a problem.

18. 8.5 Neural Network Training: A Detailed View

19. The Backpropagation Algorithm: An Example

20. Equation 8.3 Backpropagation Error Output Layer

21. Equation 8.4 Backpropagation Error Output Layer

22. Equation 8.5 Backpropagation Error Hidden Layer

23. Equations 8.6 and 8.7 The Delta Rule

24. Equation 8.8 Root Mean Squared Error

25. Kohonen Self-Organizing Maps: An Example

26. Figure 8.4 Connections for two output layer nodes

27. Equation 8.9 Classifying a New Instance Output Node = j

28. Equation 8.10 Adjusting the Weight Vectors Output Node = j

29. Building Neural Networks with iDA Chapter 9

30. 9.1 A Four-Step Approach for Backpropagation Learning 1.Prepare the data to be mined. 2.Define the network architecture. 3.Watch the network train. 4.Read and interpret summary results.

31. Example 1: Modeling the Exclusive-OR Function

33. Figure 9.1A graph of the XOR function

34. Step 1: Prepare The Data To Be Mined

35. Figure 9.2 XOR training data

36. Step 2: Define The Network Architecture

37. Figure 9.3 Dialog box for supervised learning

38. Figure 9.4 Training options for backpropagation learning

39. Step 3: Watch The Network Train

40. Figure 9.5 Neural network execution window

41. Step 4: Read and Interpret Summary Results

42. Figure 9.6 XOR output file for Experiment 1

43. Figure 9.7 XOR output file for Experiment 2

44. Example 2: The Satellite Image Dataset

45. Step 1: Prepare The Data To Be Mined

46. Figure 9.8 Satellite image data

47. Step 2: Define The Network Architecture

48. Figure 9.9 Backpropagation learning parameters for the satellite image data

49. Step 3: Watch The Network Train

50. Step 4: Read And Interpret Summary Results

51. Figure 9.10 Statistics for the satellite image data

52. Figure 9.11 Satellite image data: Actual and computed output

53. 9.2 A Four-Step Approach for Neural Network Clustering

54. Step 1: Prepare The Data To Be Mined The Deer Hunter Dataset

55. Step 2: Define The Network Architecture

56. Figure 9.12 Learning parameters for unsupervised clustering

57. Step 3: Watch The Network Train

58. Figure 9.13 Network execution window

59. Step 4: Read And Interpret Summary Results

60. Figure 9.14 Deer hunter data: Unsupervised summary statistics

61. Figure 9.15 Output clusters for the deer hunter dataset

62. 9.3 ESX for Neural Network Cluster Analysis