Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Organizational Learning in the Context of an Agri-Food Chain

This paper reports on the outcomes of the first stage of a longitudinal study that focused on the transformational change process being undertaken within the Supply Chain and Operations Area of a major Australian food manufacturing company. Organizational learning is an essential prerequisite for any successful change process and an organization's ability to learn is dependent on the existence of an environment within the organization that nurtures learning and the presence of key enablers that facilitate the learning process. An organization's capacity to learn can be enhanced through its ability to form and sustain collaborative relationships with its chain partners. The results show that an environment that supports organizational learning is being developed through consultative leadership and the empowerment of individuals within a culture that supports innovation and cross-functional teamwork but demands responsibility and accountability. The impact of these changes within the Supply Chain and Operations Area is evident in the significant improvement in the Area's productivity and efficiency levels over the past twelve months. The company's endeavours to engage its major supply chain partners in the learning process have been limited by the turmoil within the company. However the company has involved its supply chain partners in a series of mutually beneficial projects that have improved communication and built trust thereby laying the foundations for more collaborative chain relationships.

From a Vicious Circle of Anxiety to a Virtuous Circle of Learning: Experience of Teaching to a Heterogenous Clientele

A considerable body of literature suggests that significant psychological barrier and anxiety characterize the teaching and learning process in statistics. This study investigates the incidence of statistics anxiety, the extent to which it can be overcome and the factors that contribute to the process of overcoming it. Self-study and overall teaching quality, amongst others, significantly contributed to this outcome. This study identifies factors contributing to overall teaching quality. The teaching and learning process typified a highly effective communication mechanism based on an appropriate diagnosis of individual needs. This cumulative change resulted from circular causation. It is argued that given appropriate conditions the vicious circle of anxiety can be transformed into a virtuous circle of learning.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Student characteristics and academic success: An econometric study of the performance of "equity groups"

Like previous volumes in the Educational Innovation in Economics and Business Series, this book is genuinely international in terms of its coverage. With contributions from nine different countries and three continents, it reflects a global interest in, and commitment to, innovation in business education, with a view to enhancing the learning experience of both undergraduates and postgraduates. It should prove of value to anyone engaged directly in business education, defined broadly to embrace management, finance, marketing, economics, informational studies, and ethics, or who has responsibility for fostering the professional development of business educators. The contributions have been selected with the objective of encouraging and inspiring others as well as illustrating developments in the sphere of business education. This volume brings together a collection of articles describing different aspects of the developments taking place in today’s workplace and how they affect business education. It describes strategies for breaking boundaries for global learning. These target specific techniques regarding teams and collaborative learning, transitions from academic settings to the workplace, the role of IT in the learning process, and program-level innovation strategies. This volume addresses issues faced by professionals in higher and further education and also those involved in corporate training centers and industry.

Advances in application of climate prediction in agriculture

Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems. In this paper, we outline the basis for climate prediction, with emphasis on the El Nino-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction. In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary Knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based oil simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction. We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential. (C) 2001 Elsevier Science Ltd. All rights reserved.