University of Birmingham

Birmingham, United Kingdom

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School of Biosciences
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School of Psychology
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Total Impact Points
229
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School of Geography, Earth and Environmental Sciences
1,111
Total Impact Points
193
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Publication History View all

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    ABSTRACT: The classical machinery of supervised learning machines relies on a correct set of training labels. Unfortunately, there is no guarantee that all of the labels are correct. Labelling errors are increasingly noticeable in today׳s classification tasks, as the scale and difficulty of these tasks increases so much that perfect label assignment becomes nearly impossible. Several algorithms have been proposed to alleviate the problem of which a robust Kernel Fisher Discriminant is a successful example. However, for classification, discriminative models are of primary interest, and rather curiously, the very few existing label-robust discriminative classifiers are limited to linear problems. In this paper, we build on the widely used and successful kernelising technique to introduce a label-noise robust Kernel Logistic Regression classifier. The main difficulty that we need to bypass is how to determine the model complexity parameters when no trusted validation set is available. We propose to adapt the Multiple Kernel Learning approach for this new purpose, together with a Bayesian regularisation scheme. Empirical results on 13 benchmark data sets and two real-world applications demonstrate the success of our approach.
    Pattern Recognition. 11/2014; 47(11):3641–3655.
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    ABSTRACT: Hydrogen production through fuel reforming can be used to improve IC (internal combustion) engines combustion characteristics and to lower vehicle emissions. In this study, a computational fluid dynamics (CFD) model based on a detailed kinetic mechanism was developed for exhaust gas reforming of biogas to synthetic gas (H2 and CO). In agreement with experimental data, the reactor's physical and chemical performance was investigated at various O2/CH4 ratios and gas hourly space velocities (GHSV). The numerical results imply that methane reforming reactions are strongly sensitive to O2/CH4 ratio and engine exhaust gas temperature. It was also found that increasing GHSV results in lower hydrogen yield; since dry and steam reforming reactions are relatively slow and are both dependent on the flow residence time. Furthermore, the hot spot effect, which is associated to oxidation reforming reactions, was investigated for catalyst activity and durability.
    International Journal of Hydrogen Energy 08/2014; 39(24):12532–12540.
  • [Show abstract] [Hide abstract]
    ABSTRACT: http://etheses.bham.ac.uk/4911/ Process mining algorithms use event logs to learn and reason about business processes. Although process mining is essentially a machine learning task, little work has been done on systematically analysing algorithms to understand their fundamental properties, such as how much data is needed for confidence in mining. Nor does any rigorous basis exist on which to choose between algorithms and representations, or compare results. We propose a framework for analysing process mining algorithms. Processes are viewed as distributions over traces of activities and mining algorithms as learning these distributions. We use probabilistic automata as a unifying representation to which other representation languages can be converted. To validate the theory we present analyses of the Alpha and Heuristics Miner algorithms under the framework, and two practical applications. We propose a model of noise in process mining and extend the framework to mining from ?noisy? event logs. From the probabilities and sub-structures in a model, bounds can be given for the amount of data needed for mining. We also consider mining in non-stationary environments, and a method for recovery of the sequence of changed models over time. We conclude by critically evaluating this framework and suggesting directions for future research.
    School of Computer Science, University of Birmingham, 07/2014, Degree: PhD, Supervisor: Behzad Bordbar, Peter Tino

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    Edgbaston, B15 2TT, Birmingham, United Kingdom
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    www.birmingham.ac.uk
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12/2007: pages 35-50;
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01/2009; SAGE. ISBN: 9781412908344, 1412908345
109 Downloads

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