Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks

A new continuous learning method is used to optimise the selection of services in response to user requests in an active computer network simulation environment. The learning is an enhanced version of the ‘snap-drift’ algorithm, which employs the complementary concepts of fast, minimalist (snap) learning and slower drift (towards the input patterns) learning, in a non-stationary environment where new patterns arrive continually. Snap is based on Adaptive Resonance Theory, and drift on Learning Vector Quantisation. The new algorithm swaps its learning style between these two self-organisational modes when declining performance is detected, but maintains the same learning mode during episodes of improved performance. Performance updates occur at the end of each epoch. Reinforcement is implemented by enabling learning on any given pattern with a probability that increases linearly with declining performance. This method, which is capable of rapid re-learning, is used in the design of a modular neural network system: Performance-guided Adaptive Resonance Theory (P-ART). Simulations involving a requirement to continuously adapt to make appropirate decisions within a BT active computer network environment, demonstrate the learning is stable, and able to discover alternative solutions in rapid response to new performance requirements or significant changes in the stream of input patterns

Deep Learning in an Adaptive Function Neural Network

Artificial neural network learning is typically accomplished via adaptation between neurons. This paper describes adaptation that is simultaneously between and within neurons. The conventional neurocomputing wisdom is that by adapting the pattern of connections between neurons the network can learn to respond differentially to classes of incoming patterns. The success of this approach in an age of massively increasing computing power that has made high speed neurocomputing feasible on the desktop and more recently in the palm of the hand, has resulted in little attention being paid to the implications of adaptation within the individual neurons. The computational assumption has tended to be that the internal neural mechanism is fixed. However, there are good computational and biological reasons for examining the internal neural mechanisms of learning. Recent neuroscience suggests that neuromodulators play a role in learning by modifying the neuron’s activation function [Scheler] and with an adaptive function approach it is possible to learn linearly inseparable problems fast, even without hidden nodes. The ADaptive FUction Neural Network (ADFUNN) presented in this paper is based on a linear piecewise neuron activation function that is modified by a novel gradient descent supervised learning algorithm [Palmer-Brown;Kang]. It has been applied to the Iris dataset, and a natural language phrase recognition problem, exhibiting impressive generalisation classification ability with no hidden neurons

Combining neural modes of learning for handwritten digit recognition

An ADaptive Function Neural Network (ADFUNN) is combined with the on-line snapdrift learning method in this paper to perform optical and pen-based recognition of handwritten digits. Snap-Drift employs the complementary concepts of minimalist common feature learning (snap) and vector quantization (drift towards the input patterns), and is a fast unsupervised method suitable for real-time learning and non-stationary environments where new patterns are continually introduced. The ADaptive FUction Neural Network (ADFUNN) is based on a linear piecewise neuron activation function that is modified by a gradient descent supervised learning algorithm. It has previously been applied to the Iris dataset, and a natural language phrase recognition problem, exhibiting impressive generalisation classification ability without the hidden neurons that are usually required for linearly inseparable data. The unsupervised single layer Snap-Drift is effective in extracting distinct features from the complex cursive-letter datasets, and the supervised single layer ADFUNN is capable of solving linearly inseparable problems rapidly. In combination within one network (SADFUNN), these two methods are more powerful and yet simpler than MLPs (a standard neural network), at least on this problem domain. The optical and pen-based handwritten digits data are from UCI machine learning repository. The classifications are learned rapidly and produce higher generalisation results than a MLP with standard learning methods

Modal Learning in a Neural Network

This paper presents an application of the snap-drift modal learning algorithm developed in recent years by Lee and Palmer-Brown (Lee, 2004a). The application involves phrase recognition using a set of phrases from the Lancaster Parsed Corpus (LPC) (Garside, 1987). The learning algorithm is the classifier version of snap-drift. The twin modes of minimalist learning (snap) and slow drift towards the input pattern are applied alternately. Each neuron of the Snap-Drift Neural Network (SDNN) swaps between snap and drift modes when declining performance is indicated on that particular node, so that each node has its learning mode toggled independently of the other nodes. Learning on each node is also reinforced by enabling learning with a probability that decreases with increasing performance. The simulations demonstrate that learning is stable, and the results have consistently shown similar classification performance and advantages in terms of speed in comparison with a Multilayer Perceptron (MLP) and back-propagation neural networks applied to the same problem

The Analysis of Network Manager’s Behaviour using a Self-Organising Neural Networks

We present a novel neural network method for the analysis and interpretation of data that describes user interaction with a training tool. The method is applied to the interaction between trainee network managers and a simulated network management system. A simulation based approach to the task of efficiently training network managers, through the use of a simulated network, was originally presented by Pattinson [2000]. The motivation was to provide a tool for exposing trainee network managers to a life like situation, where both normal network operation and ‘fault’ scenarios could be simulated in order to train the network manager. The data logged by this system describes the detailed interaction between trainee network manager and simulated network. The work presented here provides an analysis of this interaction data that enables an assessment of the capabilities of the network manager as well as an understanding of how the network management tasks are being approached. A neural network architecture [Lee, Palmer-Brown, Roadknight 2004] is adapted and implemented in order to perform an exploratory data analysis of the interaction data. The neural network architecture employs a novel form of continuous self-organisation to discover key features, and thus provide new insights into the data

Feature discovery using snap-drift neural networks

This paper introduces an application of Snap-Drift Neural Networks (SDNNs), which employs the complementary concepts of fast, minimalist (snap) learning and slow (drift towards the input pattern) learning, for feature discovery and classification of speech waveforms from nonstammering and stammering speakers. The speech waveforms are drawn from a phonetically annotated corpus, which facilitates phonetic interpretation of the classes of patterns discovered by the SDNN. The results show that SDNN groups the phonetics speech input patterns meaningfully and extracts properties which are common to both non-stammering and stammering speech, as well as distinct features that are common within each of the utterance groups, thus supporting classification. SDNN is also being applied in a virtual learning environment to categorise students’ test responses and thereby support individualised feedback

Phonetic Feature Discovery in Speech using Snap-Drift

This paper presents a new application of the snapdrift algorithm [1]: feature discovery and clustering of speech waveforms from nonstammering and stammering speakers. The learning algorithm is an unsupervised version of snapdrift which employs the complementary concepts of fast, minimalist learning (snap) & slow drift (towards the input pattern) learning. The SnapDrift Neural Network (SDNN) is toggled between snap and drift modes on successive epochs. The speech waveforms are drawn from a phonetically annotated corpus, which facilitates phonetic interpretation of the classes of patterns discovered by the SDNN

An optimised competency framework to prepare students for employment

The language of competency is heavily utilised by employers when considering staff selection, appraisal, continued professional development, technical training and personal development. However, students and new graduates are not proficient in this language and therefore face challenges when entering the employment market. Competency frameworks exist in virtually all professional and employment sectors, but are particularly prolific in science, medicine, engineering, computing and IT, where they are often aligned to continuing professional development and certification. In this paper, we present a competency framework developed by adapting a number of existing professional competency frameworks used within the IT industry. Our competency framework is designed to be used by and for students on a degree programme with an embedded work-related learning course. The framework has two specific aims: firstly, that it must be usable by students for self-evaluation and self-regulation purposes, and secondly, that it must allow for the support and dispensing of developmental feedback. We also present the results of a study conducted to test the competency framework with 125 students on a Computing-related degree. Understanding, through cluster and correlation analysis, the way in which students perceive their own competencies has led us to optimise our framework to include the twelve most significant competencies within the Academic, Workplace and Personal Effectiveness categories. In our study, it is the Personal Effectiveness competencies such as ‘self-management’ ‘adaptability’ and ‘integrity’ that feature prominently and it is this category of competencies that students find the most challenging to refine

Classifying and evaluating assessment feedback practices

The provision of assessment feedback to students is an area which has received much interest in modern education, particularly in the Higher Education context. As current pedagogic practices strongly encourage the provision of feedback and given also the advances in digital technology, feedback mechanisms are becoming ever more sophisticated. However, considering that a great deal of effort is expended on timely, actionable and constructive feedback by tutors, the student perception of the value of the feedback given to them is not as positive as it could be. Currently a multitude of feedback practices have been developed and utilised, though with varying degrees of productiveness. Research in this area is understandably extremely broad as subject disciplines, use of technology, assessment types, methods and tools, educator preferences, student audience and peer and self-assessment capability all have a significant part to play. Given that the approaches to providing feedback are myriad, it is desirable to advance a systematic method of understanding the most constructive feedback types. This paper describes the development of a taxonomical classification which provides structure, order and frame to current popular practices that have evolved during the last decade. The taxonomy is then evaluated with the use of dimensions such as effectiveness/impact, satisfaction, adoption/engagement and quantity of feedback. The main finding of the taxonomical evaluation is the significance of developmental feed-forward guidance with which students are able to self-regulate and evaluate themselves. The paper concludes that this powerful combination should underpin further investigations into how assessment and feedback provision can be optimised for the experiential learning domain in general and to the work-based learning area in particular

Creating Intelligent Markets for SMEs Using the Snap-Drift Algorithm: A Higher Education College Perspective

The further and higher educational college (HEC) markets within the United Kingdom are considered to be dwindling. This has made it extremely difficult for private colleges to attract students as well as to provide a medium for alternate education within Britain. We present our research findings having conducted an extensive case study of a private college providing higher educational services within greater London. The research also provides a platform for determining the merits of using artificial neural networks within this sub area of education provision. In order to demonstrate a case for the integration of neural systems in this type of market we explicitly consider the snap-drift algorithm for determining likely benefits for creating intelligent markets in private colleges of higher education