### Table 2: Linguistic variables chosen for the Bayesian prediction of consonant duration.

"... In PAGE 2: ...Table 2: Linguistic variables chosen for the Bayesian prediction of consonant duration. Hence, for our Bayesian model we selected the 8 linguistic factors shown in Table2 . Consonant identity was encoded as a compound variable MV that represents manner of production and voicing distinctive features; it takes on values: voiceless stops, voiceless affricates, approximants, voiceless fricatives, nasals, voiced stops, voiced affricates, voiced fricatives and liq- uids.... ..."

### Table 1 Example: discrete model of trust dynamics

2003

"... In PAGE 10: ...Jonker and Treur, 1999). In this model trust (e.g., in somebody selling special fruit offers) has three possible states (distrust, indifferent, trust). To keep complexity limited, only the current experience and the experiences two steps back in history are taken into account to determine a trust state at time point t, according to Table1 below. Table 1 Example: discrete model of trust dynamics ... ..."

Cited by 14

### Table 1 Example: discrete model of trust dynamics

2003

"... In PAGE 10: ...Jonker and Treur, 1999). In this model trust (e.g., in somebody selling special fruit offers) has three possible states (distrust, indifferent, trust). To keep complexity limited, only the current experience and the experiences two steps back in history are taken into account to determine a trust state at time point t, according to Table1 below. Table 1 Example: discrete model of trust dynamics ... ..."

Cited by 14

### Table 1: Dependency probability representations in a Bayesian network

2003

"... In PAGE 2: ... 3 Conditional Probability Representation Before presenting the augmentation algorithms allowing mixed-mode data, it is necessary to investigate how to model the dependency between two variables with arbitrary types. Table1 summarizes the probability representation models used in this work. The methods in [1, 5] are refered in this work.... ..."

### Table 9: Estimated Gender Differences in Quits

"... In PAGE 23: ... This applies both to quits to another job and quits out of the workforce. Table9 reports our estimates of gender differences in the hazard of quitting the current job post-promotion, after controlling for a large number of characteristics. The numbers in Table 9 are hazard ratios rather than coefficients.... In PAGE 23: ...en. This applies both to quits to another job and quits out of the workforce. Table 9 reports our estimates of gender differences in the hazard of quitting the current job post-promotion, after controlling for a large number of characteristics. The numbers in Table9 are hazard ratios rather than coefficients. Therefore a value of unity to... In PAGE 24: ...interpreted as a lower female than male quit rate, while a value greater than unity represents a higher female quit rate. As with the raw data, the results in Table9 reveal that there are positive but insignificantly higher quit rates for promoted women (compared to promoted men) to another job or out of the workforce, and higher quits of unpromoted women (compared to unpromoted men) out of the workforce. In contrast to the raw data, women who have never been promoted during the sample period are less likely to quit to another job than men.... ..."

### Table 6: Complexity of the Bayesian network classiflers and C4.5.

2004

"... In PAGE 19: ... Besides looking at the classiflcation performance, we also investigated the complexity of the gen- erated classiflcation models because from a marketing viewpoint, easy to understand, parsimonious models are to be preferred. Table6 presents the complexity of the generated Bayesian network and C4.5(rules) classiflers.... ..."

Cited by 6

### Table 2: Features of Bayesian Networks

2000

"... In PAGE 3: ...ow (i.e. quot;small quot;) and the probability that it is high (i.e. quot;large quot;). Bayesian networks have a number of features that make them suitable for product design, as shown in Table2 and discussed in the remainder of this section. 3.... ..."

Cited by 5

### Table 2: Features of Bayesian Networks

2000

"... In PAGE 3: ...ow (i.e. quot;small quot;) and the probability that it is high (i.e. quot;large quot;). Bayesian networks have a number of features that make them suitable for product design, as shown in Table2 and discussed in the remainder of this section. 3.... ..."

Cited by 5

### Table 4: Weight discretization in other neural network models.

"... In PAGE 5: ...2 Quantization E ects in Other Neural Network Models Also for other neural network models the e ects of a coarse quantization of the weightvalues on recall and learning have been investigated. The small number of weight discretization algorithms proposed can be partly explained from the fact that the required accuracy for successful learning in these models is lower than for gradient descent learning in multilayer networks ( Table4 ). An interesting example of a hardware implementation is Bellcore apos;s implementation of a Boltzmann machine and Mean-Field learning, whichallows on-chip learning with only 5-bit weights [Alspector-92].... ..."

### Table 5 Bayesian Network Model and the Importance Ranking of the Fourteen Dimensions of Growth-oriented Atmosphere

"... In PAGE 16: ... We expected on the grounds of the correlational analysis that all but the first group of variables representing empowerment dimensions would cluster close to each other. The Bayesian network solution is presented in Table5 . The left hand side shows visualization of the network where nodes represent variables and arches dependencies between them.... ..."