anytime algorithms for learning anytime classifiers saher ... - Technion

Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008
List of Tables
3.1 Possible training set for learning the 2-XOR concept . . . . . . . . 21
3.2 Characteristics of the datasets used . . . . . . . . . . . . . . . . . 46
3.3 The size of the induced trees on various datasets . . . . . . . . . . 49
3.4 The differences in the size of the induced trees on various datasets 50
3.5 The generalization accuracy of the induced trees on various datasets 51
3.6 The differences in generalization accuracy of the induced trees on
various datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.1 Characteristics of the datasets used to evaluate ACT . . . . . . . 82
4.2 Average cost of classification for different mc values . . . . . . . . 85
4.3 DTMC vs. ACT and ICET vs. ACT using statistical tests . . . . 86
4.4 Average cost of classification for mc = 100 . . . . . . . . . . . . . 87
4.5 Average cost of classification for mc = 500 . . . . . . . . . . . . . 88
4.6 Average cost of classification for mc = 1000 . . . . . . . . . . . . 89
4.7 Average cost of classification for mc = 5000 . . . . . . . . . . . . 90
4.8 Average cost of classification for mc = 10000 . . . . . . . . . . . . 91
4.9 Average cost when test costs are assigned randomly . . . . . . . . 93
4.10 Comparison of various algorithms when error costs are nonuniform 94
5.1 Characteristics of the datasets used to evaluate TATA . . . . . . . 112
5.2 Comparing the misclassification cost for different contracts . . . . 113
B.1 Testing Occam’s empirical principle for consistent trees . . . . . . 149
B.2 Testing Occam’s empirical principle when the trees are pruned . . 151