The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Decision support to green technology implementation on Australian university campuses

As the societal awareness on sustainability is gaining momentum worldwide, the higher education sector is expected to take the lead in education, research and the promotion of sustainable development. Universities have the diversity of skills and knowledge to explore new concepts and issues, the academic freedom to offer unbiased observations, and the capacity to engage in experimentation for solutions. There is a global trend that universities have realized and responded to sustainability challenge. By adopting green technologies, buildings on university campuses have the potential to offer highly productive and green environments for a quality learning experience for students, while minimising environmental impacts. Despite the potential benefits and metaphorical link to sustainability, few universities have moved towards implementing Green Roof and Living Wall on campuses widely, which have had more successful applications in commercial and residential buildings. Few past research efforts have examined the fundamental barriers to the implementation of sustainable projects on campuses from organizational level. To address this deficiency, an on-going research project is undertaken by Queensland University of Technology in Australia. The research is aimed at developing a comprehensive framework to facilitate better decision making for the promotion of Green Roof and Living Wall application on campuses. It will explore and highlight organizational factors as well as investigate and emphasize project delivery issues. Also, the critical technical indicators for Green Roof and Living Wall implementation will be identified. The expected outcome of this research has the potential to enhance Green Roof and Living Wall delivery in Australian universities, as a vital step towards realizing sustainability in higher education sectors.

Overcoming the complexity of diagnostic problems due to sensor network architecture

In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system’s health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.

Group maintenance scheduling : a case study for a pipeline network

This paper presents a group maintenance scheduling case study for a water distributed network. This water pipeline network presents the challenge of maintaining aging pipelines with the associated increases in annual maintenance costs. The case study focuses on developing an effective maintenance plan for the water utility. Current replacement planning is difficult as it needs to balance the replacement needs under limited budgets. A Maintenance Grouping Optimization (MGO) model based on a modified genetic algorithm was utilized to develop an optimum group maintenance schedule over a 20-year cycle. The adjacent geographical distribution of pipelines was used as a grouping criterion to control the searching space of the MGO model through a Judgment Matrix. Based on the optimum group maintenance schedule, the total cost was effectively reduced compared with the schedules without grouping maintenance jobs. This optimum result can be used as a guidance to optimize the current maintenance plan for the water utility.

Ensuring student success : the role of support services in improving the quality of the student learning experience

This paper is based on the premise that universities have an obligation to provide adequate student support services, such as learning assistance (that is, assistance with academic writing and other study skills) and that in order to be effective such services must be responsive to the wider policy and social implications of student attrition and retention. The paper outlines briefly some of the factors that have influenced the development of learning assistance practices in Australia and America. This is followed by an account of experiences at one Australian metropolitan university where learning assistance service provision shifted from a decentralised, faculty-based model to a centralised model of service delivery. This shift was in response to concerns about lack of quality and consistency in a support model dependent upon faculty resources yet a follow up study identified other problems in the centralised delivery of learning assistance services. These problems, clustered under the heading contextualised versus decontextualised learning assistance, include the relevance of generic learning assistance services to students struggling with specific course related demands; the apparent tensions between challenging students and assisting students at risk of failure; and variations in the level of collaboration between learning advisers and academic staff in supporting students in the learning environment. These problems are analysed using the theoretical modelling derived from the tools made available through cultural historical activity theory and expansive visibilisation (Engeström & Miettinen, 1999).

The Chronic Heart-Failure Assistance by Telephone (CHAT) Study: Assessment of Telephone Support for Vulnerable Patients with Chronic Disease

Aim: To determine whether telephone support using an evidence-based protocol for chronic heart failure (CHF) management will improve patient outcomes and will reduce hospital readmission rates in patients without access to hospital-based management programs. Methods: The rationale and protocol for a cluster-design randomised controlled trial (RCT) of a semi-automated telephone intervention for the management of CHF, the Chronic Heart-failure Assistance by Telephone (CHAT) Study is described. Care is coordinated by trained cardiac nurses located in Heartline, the national call center of the National Heart Foundation of Australia in partnership with patients’ general practitioners (GPs). Conclusions: The CHAT Study model represents a potentially cost-effective and accessible model for the Australian health system in caring for CHF patients in rural and remote areas. The system of care could also be readily adapted for a range of chronic diseases and health systems. Key words: chronic disease management; chronic heart failure; integrated health care systems; nursing care, rural health services; telemedicine; telenursing

Nursing Sans Frontieres: A Three Year Case Study of Multi-State Registration of Support Nursing Practice Using Information Technology

Objective: To highlight the registration issues for nurses who wish to practice nationally, particularly those practicing within the telehealth sector. Design: As part of a national clinical research study, applications were made to every state and territory for mutual recognition of nursing registration and fee waiver for telenursing cross boarder practice for a period of three years. These processes are described using a case study approach. Outcome: The aim of this case study was to achieve registration in every state and territory of Australia without paying multiple fees by using mutual recognition provisions and the cross-border fee waiver policy of the nurse regulatory authorities in order to practice telenursing. Results: Mutual recognition and fee waiver for cross-border practice was granted unconditionally in two states: Victoria (Vic) and Tasmania (Tas), and one territory: the Northern Territory (NT). The remainder of the Australian states and territories would only grant temporary registration for the period of the project or not at all, due to policy restrictions or nurse regulatory authority (NRA) Board decisions. As a consequence of gaining fee waiver the annual cost of registration was a maximum of $145 per annum as opposed to the potential $959 for initial registration and $625 for annual renewal. Conclusions: Having eight individual nurses Acts and NRAs for a population of 265,000 nurses would clearly indicate a case for over regulation in this country. The structure of regulation of nursing in Australia is a barrier to the changing and evolving role of nurses in the 21st century and a significant factor when considering workforce planning.