Description and training of neural network dynamics

Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.

Efficient training of RBF networks for classification.

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.

Bayesian training of mixture density networks

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

Efficient training of RBF networks for classification

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

Professional encounters in TESOL:Discourses of teachers in teaching

This book investigates the developing discourses of English Language teachers in a variety of international contexts. By analysing how professional development takes place through participation in professional discourse, the chapters shed light on what teachers do and why they do it.

An investigation into the nature and consequences of teachers' implicit philosophies of science

The aims of this study were to investigate the beliefs concerning the philosophy of science held by practising science teachers and to relate those beliefs to their pupils' understanding of the philosophy of science. Three philosophies of science, differing in the way they relate experimental work to other parts of the scientific enterprise, are described. By the use of questionnaire techniques, teachers of four extreme types were identified. These are: the H type or hypothetico-deductivist teacher, who sees experiments as potential falsifiers of hypotheses or of logical deductions from them; the I type or inductivist teacher, who regards experiments mainly as a way of increasing the range of observations available for recording before patterns are noted and inductive generalisation is carried out; the V type or verificationist teacher, who expects experiments to provide proof and to demonstrate the truth or accuracy of scientific statements; and the 0 type, who has no discernible philosophical beliefs about the nature of science or its methodology. Following interviews of selected teachers to check their responses to the questionnaire and to determine their normal teaching methods, an experiment was organised in which parallel groups were given H, I and V type teaching in the normal school situation during most of one academic year. Using pre-test and post-test scores on a specially developed test of pupil understanding of the philosophy of science, it was shown that pupils were positively affected by their teacher's implied philosophy of science. There was also some indication that V type teaching improved marks obtained in school science examinations, but appeared to discourage the more able from continuing the study of science. Effects were also noted on vocabulary used by pupils to describe scientists and their activities.

An initial evaluation of gellan gum as a material for tissue engineering applications

Alpha-modified minimum essential medium (αMEM) has been found to cross-link a 1% gellan gum solution, resulting in the formation of a self-supporting hydrogel in 1:1 and 5:1 ratios of polysaccharide: αMEM. Rheological data from temperature sweeps confirm that in addition to orders of magnitude differences in G' between 1% gellan and 1% gellan with αMEM, there is also a 20°C increase in the temperature at which the onset of gelation takes place when αMEM is present. Frequency sweeps confirm the formation of a true gel; mechanical spectra for mixtures of gellan and αMEM clearly demonstrate G' to be independent of frequency. It is possible to immobilize cells within a three-dimensional (3D) gellan matrix that remain viable for up to 21 days in culture by adding a suspension of rat bone marrow cells (rBMC) in αMEM to 1% gellan solution. This extremely simple approach to cell immobilization within 3D constructs, made possible by the fact that gellan solutions cross-link in the presence of millimolar concentrations of cations, poses a very low risk to a cell population immobilized within a gellan matrix and thus indicates the potential of gellan for use as a tissue engineering scaffold. © 2007 Sage Publications.

Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.

The use of open learning in the training of neurotechnologists:a regional case study

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The education and training of technicians in the construction industry

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The effectiveness of an organisations' equal opportunities policy for the role and training of women, particularly in the fire service

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