Playing in continuous spaces: Some analysis and extension of population-based incremental learning

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.

Prediction with Gaussian processes: from linear regression to linear prediction and beyond

The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. This leads in to a more general discussion of Gaussian processes in section 4. Section 5 deals with further issues, including hierarchical modelling and the setting of the parameters that control the Gaussian process, the covariance functions for neural network models and the use of Gaussian processes in classification problems.

Gaussian processes:iterative sparse approximations

In recent years there has been an increased interest in applying non-parametric methods to real-world problems. Significant research has been devoted to Gaussian processes (GPs) due to their increased flexibility when compared with parametric models. These methods use Bayesian learning, which generally leads to analytically intractable posteriors. This thesis proposes a two-step solution to construct a probabilistic approximation to the posterior. In the first step we adapt the Bayesian online learning to GPs: the final approximation to the posterior is the result of propagating the first and second moments of intermediate posteriors obtained by combining a new example with the previous approximation. The propagation of em functional forms is solved by showing the existence of a parametrisation to posterior moments that uses combinations of the kernel function at the training points, transforming the Bayesian online learning of functions into a parametric formulation. The drawback is the prohibitive quadratic scaling of the number of parameters with the size of the data, making the method inapplicable to large datasets. The second step solves the problem of the exploding parameter size and makes GPs applicable to arbitrarily large datasets. The approximation is based on a measure of distance between two GPs, the KL-divergence between GPs. This second approximation is with a constrained GP in which only a small subset of the whole training dataset is used to represent the GP. This subset is called the em Basis Vector, or BV set and the resulting GP is a sparse approximation to the true posterior. As this sparsity is based on the KL-minimisation, it is probabilistic and independent of the way the posterior approximation from the first step is obtained. We combine the sparse approximation with an extension to the Bayesian online algorithm that allows multiple iterations for each input and thus approximating a batch solution. The resulting sparse learning algorithm is a generic one: for different problems we only change the likelihood. The algorithm is applied to a variety of problems and we examine its performance both on more classical regression and classification tasks and to the data-assimilation and a simple density estimation problems.

Engaging tutors in using e-repositories for learning and teaching

Universities which set up online repositories for the management of learning and teaching resources commonly find that uptake is poor. Tutors are often reluctant to upload their materials to e-repositories, even though the same tutors are happy to upload resources to the virtual learning environment (e.g. Blackboard, Moodle, Sakai) and happy to upload their research papers to the university’s research publications repository. The paper reviews this phenomenon and suggests constructive ways in which tutors can be encouraged to engage with an e-repository. The authors have recently completed a major project “Developing Repositories at Worcester” which is part of a group of similar projects in the UK. The paper includes the feedback and the lessons learned from these projects, based on the publications and reports they have produced. They cover ways of embedding repository use into institutional working practice, and give examples of different types of repository designed to meet the needs of those using different kinds of learning and teaching resources. As well as this specific experience, the authors summarise some of the main findings from UK publications, in particular the December 2008 report of Joint Information Systems Committee: Good intentions: improving the evidence base in support of sharing learning materials and Online Innovation in Higher Education, Ron Cooke’s report to a UK government initiative on the future of Higher Education. The issues covered include the development of Web 2.0 style repositories rather than conventionally structured ones, the use of tags rather than metadata, the open resources initiative, the best use for conventional repositories, links to virtual learning environments, and the processes for the management and support of repositories within universities. In summary the paper presents an optimistic, constructive view of how to embed the use of e-repositories into the working practices of university tutors. Equally, the authors are aware of the considerable difficulties in making progress and are realistic about what can be achieved. The paper uses evidence and experience drawn from those working in this field to suggest a strategic vision in which the management of e-learning resources is productive, efficient and meets the needs of both tutors and their students.

Policy into practice:a study of public policy implementation and the role of learning

Public policy becomes managerial practice through a process of implementation. There is an established literature within Implementation Studies which explains the variables and some of the processes involved in implementation, but less attention has been focused upon how public service managers convert new policy initiatives into practice. The research proposes that managers and their organisations have to go through a process of learning in order to achieve the implementation of public policy. Data was collected over a five year period from four case studies of capital investment appraisal in the British National Health Service. Further data was collected from taped interviews by key actors within the case studies. The findings suggest that managers do learn to implement policy and four factors are important in this learning process. These are; (i) the nature of bureaucratic responsibility; (ii) the motivation of actors towards learning; (iii) the passage of time which allows for the development of competence and (iv) the use of project team structures. The research has demonstrated that the conversion of policy into practice occurs through the operationalisation of solutions to policy problems via job tasks. As such it suggests that in understanding how policy is implemented, technical learning is more important than cultural learning, in this context. In conclusion, a "Model of Learned Implementation" is presented, together with a discussion of some of the implications of the research. These are the possible use of more pilot projects for new policy initiatives and the more systematic diffusion of knowledge about implementation solutions.

Organisational learning - a critical systems thinking discipline

Original Paper European Journal of Information Systems (2001) 10, 135–146; doi:10.1057/palgrave.ejis.3000394 Organisational learning—a critical systems thinking discipline P Panagiotidis1,3 and J S Edwards2,4 1Deloitte and Touche, Athens, Greece 2Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK Correspondence: Dr J S Edwards, Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. E-mail: j.s.edwards@aston.ac.uk 3Petros Panagiotidis is Manager responsible for the Process and Systems Integrity Services of Deloitte and Touche in Athens, Greece. He has a BSc in Business Administration and an MSc in Management Information Systems from Western International University, Phoenix, Arizona, USA; an MSc in Business Systems Analysis and Design from City University, London, UK; and a PhD degree from Aston University, Birmingham, UK. His doctorate was in Business Systems Analysis and Design. His principal interests now are in the ERP/DSS field, where he serves as project leader and project risk managment leader in the implementation of SAP and JD Edwards/Cognos in various major clients in the telecommunications and manufacturing sectors. In addition, he is responsible for the development and application of knowledge management systems and activity-based costing systems. 4John S Edwards is Senior Lecturer in Operational Research and Systems at Aston Business School, Birmingham, UK. He holds MA and PhD degrees (in mathematics and operational research respectively) from Cambridge University. His principal research interests are in knowledge management and decision support, especially methods and processes for system development. He has written more than 30 research papers on these topics, and two books, Building Knowledge-based Systems and Decision Making with Computers, both published by Pitman. Current research work includes the effect of scale of operations on knowledge management, interfacing expert systems with simulation models, process modelling in law and legal services, and a study of the use of artifical intelligence techniques in management accounting. Top of pageAbstract This paper deals with the application of critical systems thinking in the domain of organisational learning and knowledge management. Its viewpoint is that deep organisational learning only takes place when the business systems' stakeholders reflect on their actions and thus inquire about their purpose(s) in relation to the business system and the other stakeholders they perceive to exist. This is done by reflecting both on the sources of motivation and/or deception that are contained in their purpose, and also on the sources of collective motivation and/or deception that are contained in the business system's purpose. The development of an organisational information system that captures, manages and institutionalises meaningful information—a knowledge management system—cannot be separated from organisational learning practices, since it should be the result of these very practices. Although Senge's five disciplines provide a useful starting-point in looking at organisational learning, we argue for a critical systems approach, instead of an uncritical Systems Dynamics one that concentrates only on the organisational learning practices. We proceed to outline a methodology called Business Systems Purpose Analysis (BSPA) that offers a participatory structure for team and organisational learning, upon which the stakeholders can take legitimate action that is based on the force of the better argument. In addition, the organisational learning process in BSPA leads to the development of an intrinsically motivated information organisational system that allows for the institutionalisation of the learning process itself in the form of an organisational knowledge management system. This could be a specific application, or something as wide-ranging as an Enterprise Resource Planning (ERP) implementation. Examples of the use of BSPA in two ERP implementations are presented.

International retail joint venture learning

Where retail entry mode decision-making is examined in the literature, it has almost exclusively focused upon international store acquisitions and franchising growth and expansion. In contrast, international joint venture decision-making processes are visibly absent in the international retail literature. This article explores three retail multinationals' international retail joint venturing experiences, extracting some of the salient lessons learned at each stage of the joint venture development process and their concurrent impact on the whole internationalisation process. Suggestions for further research are made on the basis of gaps in the international retail literature and the lessons extracted from the cases under investigation. © 2006 Taylor & Francis.

Learning in manufacturing organizations:what factors predict effectiveness?

This paper argues that it is possible to identify factors which pre-dispose organizations to adopt effective learning strategies and processes. It is hypothesized that effective OL is associated with: profitability, environmental uncertainty, structure, approach to HRM and quality orientation. The study focuses on forty-four manufacturing organizations, and draws on longitudinal data gathered through interviews. The findings suggest that two of these variables - approach to HRM and quality orientation - are particularly strongly correlated with measures of OL. It is concluded that effective learning mechanisms, with the potential to improve the quality of OL processes, are more likely to be established in businesses where HRM and quality initiatives are well established.

Technological and organisational change in small- to medium-sized manufacturing companies:a learning organisation perspective

This paper examines the extent to which a learning organisation perspective is attainable in small- to medium-sized manufacturing companies. An audit tool is developed from the literature on organisational learning and recognised processes that lead towards becoming a learning organisation. The paper focuses on the application of the audit tool in three UK automotive component suppliers which are all experiencing pressures for change imposed by the major vehicle manufacturers. The main changes are concerned with tiering of the supply chain and substantial delegation of responsibilities to component suppliers including an increasing emphasis on innovation and continuous improvement. The companies presented in the paper are taken from a research project into the impact of changes in supply chain relationships on the operation of small- and medium-sized manufacturing firms in the West Midlands region of the UK. The ways in which the companies are responding to change are presented together with the results of a self-assessment using the developed audit tool. These results suggest that companies of this type tend to focus on change in those areas that involve least challenge to the established power and authority of management.

Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.

Studies on the generalisation of Gaussian processes and Bayesian neural networks

The assessment of the reliability of systems which learn from data is a key issue to investigate thoroughly before the actual application of information processing techniques to real-world problems. Over the recent years Gaussian processes and Bayesian neural networks have come to the fore and in this thesis their generalisation capabilities are analysed from theoretical and empirical perspectives. Upper and lower bounds on the learning curve of Gaussian processes are investigated in order to estimate the amount of data required to guarantee a certain level of generalisation performance. In this thesis we analyse the effects on the bounds and the learning curve induced by the smoothness of stochastic processes described by four different covariance functions. We also explain the early, linearly-decreasing behaviour of the curves and we investigate the asymptotic behaviour of the upper bounds. The effect of the noise and the characteristic lengthscale of the stochastic process on the tightness of the bounds are also discussed. The analysis is supported by several numerical simulations. The generalisation error of a Gaussian process is affected by the dimension of the input vector and may be decreased by input-variable reduction techniques. In conventional approaches to Gaussian process regression, the positive definite matrix estimating the distance between input points is often taken diagonal. In this thesis we show that a general distance matrix is able to estimate the effective dimensionality of the regression problem as well as to discover the linear transformation from the manifest variables to the hidden-feature space, with a significant reduction of the input dimension. Numerical simulations confirm the significant superiority of the general distance matrix with respect to the diagonal one.In the thesis we also present an empirical investigation of the generalisation errors of neural networks trained by two Bayesian algorithms, the Markov Chain Monte Carlo method and the evidence framework; the neural networks have been trained on the task of labelling segmented outdoor images.

A constructive learning algorithm based on back-propagation

There are been a resurgence of interest in the neural networks field in recent years, provoked in part by the discovery of the properties of multi-layer networks. This interest has in turn raised questions about the possibility of making neural network behaviour more adaptive by automating some of the processes involved. Prior to these particular questions, the process of determining the parameters and network architecture required to solve a given problem had been a time consuming activity. A number of researchers have attempted to address these issues by automating these processes, concentrating in particular on the dynamic selection of an appropriate network architecture.The work presented here specifically explores the area of automatic architecture selection; it focuses upon the design and implementation of a dynamic algorithm based on the Back-Propagation learning algorithm. The algorithm constructs a single hidden layer as the learning process proceeds using individual pattern error as the basis of unit insertion. This algorithm is applied to several problems of differing type and complexity and is found to produce near minimal architectures that are shown to have a high level of generalisation ability.

Investigating Turkey's national innovation and learning system

The national systems of innovation (NIS) approach focuses on the patterns and the determinants of innovation processes from the perspective of nation-states. This paper reports on continuing work on the application of an NIS model to the development of technological capability in Turkey. Initial assessment of the literature shows that there are a number of alternative conceptualisations of NIS. An attempt by the Government to identify a NIS for Turkey shows the main actors in the system but does not pay sufficient attention to the processes of interactions between agents within the system. An operational model should be capable of representing these processes and interactions and assessing the strengths and weaknesses of the NIS. For industrialising countries, it is also necessary to incorporate learning mechanisms into the model. Further, there are different levels of innovation and capability in different sectors which the national perspective may not reflect. This paper is arranged into three sections. The first briefly explains the basics of the national innovation and learning system. Although there is no single accepted definition of NIS, alternative definitions reviewed share some common characteristics. In the second section, an NIS model is applied to Turkey in order to identify the elements, which characterise the country’s NIS. This section explains knowledge flow and defines the relations between the actors within the system. The final section draws on the “from imitation to innovation” model apparently so successful in East Asia and assesses its applicability to Turkey. In assessing Turkey’s NIS, the focus is on the automotive and textile sectors.