### Table 1. Performance of Bayesian Belief Network

2004

"... In PAGE 4: ... Further Bayesian network classifier is constructed using the training data and then the classifier is used on the test data set to classify the data as an attack or normal. Table1 depicts the performance of Bayesian belief network by using the original 41 variable data set and the 17 variables reduced data set. The training and testing times for each classifier are decreased when 17 variable data set is used.... ..."

Cited by 2

### Table 1. Performance of Bayesian Belief Network

2005

"... In PAGE 15: ....3.1. Modeling IDS Using Bayesian Classifier Furthermore, Bayesian network classifier is constructed using the training data and then the classifier is used on the test data set to classify the data as an attack or normal. Table1 depicts the performance of Bayesian belief network by using the original 41 variable data set and the 17 variables reduced data set. The training and testing times for each classifier are decreased when 17 variable data set is used.... ..."

Cited by 2

### Table 6: Rule Base Expressed as a Decision Table Inputs Rules

1997

"... In PAGE 19: ... After a mapping is obtained, the second step in concept formation is to convert the decision table into a compressed form. The compressed decision table for the credit granting rule base is shown in Table6 . We note that a compressed decision table is not the only representation of concepts suitable for cost optimization.... ..."

Cited by 4

### Table 2: Example Software Belief Network for CEquencer

1997

"... In PAGE 10: ... #0F Con#0Cdence levels for requirements elements have been established earlier, in Table 3. Ad- ditional con#0Cdence levels for test artifacts #28elicited from CEquencer testers#29, code modules #28elicited from CEquencer programmers#29, and their relations were collected and are summa- rized in Table2 . Figure 2 is a graphical depiction of artifacts, their relations, and associated belief values.... ..."

Cited by 5

### Table 1: Information inequality and absolute divergence of an approximated example belief network.

1997

"... In PAGE 17: ... Therefore, this graphically portrayed dependence can be rendered redundant and arc V8 ! V9 can be removed without introducing an error in the probability distribution since I(Pr; PrV86!V9) = 0 as shown in Figure 2. Table1 gives the upper bound provided by the information inequality and the absolute divergence of the approximated joint probability distributions after removal of various linear subsets of arcs A from the network apos;s digraph. The table is compressed by leaving out all linear sets containing arc V8 ! V9 (except for the set fV8 ! V9g) because the second and third column are both unchanged after leaving out this arc.... ..."

Cited by 16

### Table 1: Prior probabilities for nal, integrated belief network.

1995

Cited by 13

### Table 1: Exhaustive list of joint probabilities for a trivial belief network.

### Table 1: Alternative rankings modeled in our belief network model.

### Table 1: Results for experiments with 20 belief networks from 5 problem classes.

"... In PAGE 8: ... In these results we only applied the reduction by isomorphism and still kept the redundant meta-nodes. Table1 shows the results for 20 belief networks from 5 problem classes: medical diagnosis (CPCS), digital cir- cuits (ISCAS), deterministic grid networks (GRID), ge- netic linkage analysis (LINKAGE) as well as relational be- lief networks (PRIMULA). For each network we chose ran- domly e variables and set their values as evidence.... ..."

### Table 1: Results for experiments with 20 belief networks from 5 problem classes.

"... In PAGE 8: ... In these results we only applied the reduction by isomorphism and still kept the redundant meta-nodes. Table1 shows the results for 20 belief networks from 5 problem classes: medical diagnosis (CPCS), digital cir- cuits (ISCAS), deterministic grid networks (GRID), ge- netic linkage analysis (LINKAGE) as well as relational be- lief networks (PRIMULA). For each network we chose ran- domly e variables and set their values as evidence.... ..."