4.4.5 Variations on Backprop
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66
4.4.6 An Application: Steering a Van
::::::::::::
67
4.5 Synergies Between Neural Network and Knowledge-Based
Metho ds
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68
4.6 Bibliographical and Historical Remarks
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68
5 Statistical Learning 69
5.1 Using Statistical Decision Theory
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69
5.1.1 Background and General Metho d
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69
5.1.2 Gaussian (or Normal) Distributions
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71
5.1.3 Conditionally Indep endent Binary Comp onents
:::
75
5.2 Learning Belief Networks
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77
5.3 Nearest-Neighb or Metho ds
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77
5.4 Bibliographical and Historical Remarks
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79
6 Decision Trees 81
6.1 Denitions
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81
6.2 Sup ervised Learning of Univariate Decision Trees
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83
6.2.1 Selecting the TypeofTest
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83
6.2.2 Using Uncertainty Reduction to Select Tests
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84
6.2.3 Non-Binary Attributes
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88
6.3 Networks Equivalent to Decision Trees
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88
6.4 Overtting and Evaluation
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89
6.4.1 Overtting
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89
6.4.2 Validation Metho ds
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90
6.4.3 Avoiding Overtting in Decision Trees
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91
6.4.4 Minimum-Description Length Metho ds
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92
6.4.5 Noise in Data
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93
6.5 The Problem of Replicated Subtrees
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94
6.6 The Problem of Missing Attributes
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96
6.7 Comparisons
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96
6.8 Bibliographical and Historical Remarks
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96
iii
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