Determining the driving forces of economic growth has a paramount importance in economic studies. Early studies suggested physical capital accumulation as the main driving force of growth. However, the recent studies try to resolve the flaws of neo classical school on growth by expanding the concept of capital equipment and machinery to a wider range of human knowledge, on one hand, and in...

The primary idea of fuzzy control is to employ the knowledge of experts to control a plant instead of the algorithm derived from the mathematical model of the plant. If the parameters in the fuzzy rules, which are based on experts’ views, are changed at all, then fuzzy control loses its initiative. In this paper, we consider a few cases to show how to develop a real fuzzy control system with st...

The RKF Year 2 Challenge problem highlighted the difficulty that Subject Matter Experts (SMEs) have in writing inference rules. During the evaluation period, the KRAKEN SMEs entered about five to seven rules each [Pool et al., 2003]. One solution to this problem is to provide support for learning inference rules from examples rather than requiring domain experts to write the rules themselves. W...

Summarizing is a process of identifying important information from a text. Experts employ several important strategies to produce good summaries. Unfortunately, students do not acquire the summarization skills. Thus, this paper proposed an algorithm to identify the summarizing strategies employed by students using summary sentence decomposition. The strategies used by experts are identified and...

We consider a cross-calibration test of predictions by multiple potential experts in a stochastic environment. This test checks whether each expert is calibrated conditional on the predictions made by other experts. We show that this test is good in the sense that a true expert—one informed of the true distribution of the process—is guaranteed to pass the test no matter what the other potential...

In order to help the expert to validate association rules, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data structure from which the rules are extracted. The second one has two subcategories: The first one consists on providing to the expert a tool for rule interactive e...

1. Objectives: The aim was to evaluate the potential for monotonicity constraints to bias machine learning systems to learn rules that were both accurate and meaningful. 2. Methods: Two data sets from problems as diverse as screening for dementia and assessing the risk of mental retardation were collected and a rule learning system with and without monotonicity constraints was run on each. The ...