Machine learning has been proven useful for solving the bottlenecks in building expert systems. Noise in the training instances will, however, confuse a learning mechanism. Two main steps are adopted here to solve this problem. The first step is to appropriately arrange the training order of the instances. It is well known from Psychology that different orders of presentation of the same set of training instances to a human may cause different learning results. This idea is used here for machine (...) learning and an order arrangement scheme is proposed. The second step is to modify a conventional noise-free learning algorithm, thus making it suitable for noisy environment. The generalized version space learning algorithm is then adopted to process the training instances for deriving good concepts. Finally, experiments on the Iris Flower problem show that the new scheme can produce a good training order, allowing the generalized version space algorithm to have a satisfactory learning result. (shrink)

Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent imperfect training instances and (...) rules, and based on the new representation, a Human-Like Learning (HULL) algorithm for incrementally learning concepts well in imperfect training environments. Several examples are given to make the algorithm clearer. Finally, experimental results are presented that show the proposed learning algorithm works well in imperfect learning environments. (shrink)

This study examines the effects of leader emotional intelligence, leadership styles, organizational commitment, and trust on job performance. A questionnaire was administered to the participants, who were real estate brokers in Kaohsiung City. Of the 980 questionnaires administered, 348 valid responses were received, indicating an effective response rate of 35.5%. Structural equation modeling was used for the analysis. The results show that leader emotional intelligence has a significant and positive effect on trust in supervisors, and transformational leadership and trust within (...) a team have significant and positive effects on job performance. In addition, organizational commitment has a significant and positive effect on job performance. Trust within a team mediates the significant and positive effect of leader emotional intelligence on job performance. Although transactional leadership has no direct, positive, and significant effects on employee job performance, trust in the workplace shaped by a leader’s leadership style will empower a team’s spirit and boost their morale, thereby indirectly affecting their job performance in a positive manner. (shrink)