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  • Theoretical Computer Science 11/2014; 425:2–3.
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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: 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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    ABSTRACT: ContextEnsembles of learning machines and locality are considered two important topics for the next research frontier on Software Effort Estimation (SEE).Objectives We aim at (1) evaluating whether existing automated ensembles of learning machines generally improve SEEs given by single learning machines and which of them would be more useful; (2) analysing the adequacy of different locality approaches; and getting insight on (3) how to improve SEE and (4) how to evaluate/choose machine learning (ML) models for SEE.MethodA principled experimental framework is used for the analysis and to provide insights that are not based simply on intuition or speculation. A comprehensive experimental study of several automated ensembles, single learning machines and locality approaches, which present features potentially beneficial for SEE, is performed. Additionally, an analysis of feature selection and regression trees (RTs), and an investigation of two tailored forms of combining ensembles and locality are performed to provide further insight on improving SEE.ResultsBagging ensembles of RTs show to perform well, being highly ranked in terms of performance across different data sets, being frequently among the best approaches for each data set and rarely performing considerably worse than the best approach for any data set. They are recommended over other learning machines should an organisation have no resources to perform experiments to chose a model. Even though RTs have been shown to be more reliable locality approaches, other approaches such as k-Means and k-Nearest Neighbours can also perform well, in particular for more heterogeneous data sets.Conclusion Combining the power of automated ensembles and locality can lead to competitive results in SEE. By analysing such approaches, we provide several insights that can be used by future research in the area.
    Information and Software Technology 08/2013; 55(8):1512–1528.
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    ABSTRACT: The approach Clark labels "action-oriented predictive processing" treats all cognition as part of a system of on-line control. This ignores other important aspects of animal, human, and robot intelligence. He contrasts it with an alleged "mainstream" approach that also ignores the depth and variety of AI/Robotic research. I don't think the theory presented is worth taking seriously as a complete model, even if there is much that it explains.
    Behavioral and Brain Sciences 05/2013;
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    ABSTRACT: MOTIVATION: Previous studies reported that labelling errors are not uncommon in microarray datasets. In such cases, the training set may become misleading, and the ability of classifiers to make reliable inferences from the data is compromised. Yet, few methods are currently available in the bioinformatics literature to deal with this problem. The few existing methods focus on data cleansing alone, without reference to classification, and their performance crucially depends on some tuning parameters. RESULTS: In this article, we develop a new method to detect mislabelled arrays simultaneously with learning a sparse logistic regression classifier. Our method may be seen as a label-noise robust extension of the well-known and successful Bayesian logistic regression classifier. To account for possible mislabelling, we formulate a label-flipping process as part of the classifier. The regularization parameter is automatically set using Bayesian regularization, which not only saves the computation time that cross-validation would take, but also eliminates any unwanted effects of label noise when setting the regularization parameter. Extensive experiments with both synthetic data and real microarray datasets demonstrate that our approach is able to counter the bad effects of labelling errors in terms of predictive performance, it is effective at identifying marker genes and simultaneously it detects mislabelled arrays to high accuracy. AVAILABILITY: The code is available from http://cs.bham.ac.uk/∼jxb008. CONTACT: J.Bootkrajang@cs.bham.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    Bioinformatics 03/2013;
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    ABSTRACT: We present an approach for planning robotic manipulation tasks that uses a learned mapping between geometric states and logical predicates. Manipulation planning, because it requires task-level and geometric reasoning, requires such a mapping to convert between the two. Consider a robot tasked with putting several cups on a tray. The robot needs to find positions for all the objects, and may need to nest one cup inside another to get them all on the tray. This requires translating back and forth between symbolic states that the planner uses, such as stacked (cup1,cup2), and geometric states representing the positions and poses of the objects. We learn the mapping from labelled examples, and importantly learn a representation that can be used in both the forward (from geometric to symbolic) and reverse directions. This enables us to build symbolic representations of scenes the robot observes, but also to translate a desired symbolic state from a plan into a geometric state that the robot can achieve through manipulation. We also show how such a mapping can be used for efficient manipulation planning: the planner first plans symbolically, then applies the mapping to generate geometric positions that are then sent to a path planner.
    Robotics and Autonomous Systems 01/2013;
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    ABSTRACT: To help understand how semantic information is represented in the human brain, a number of previous studies have explored how a linear mapping from corpus derived semantic representations to corresponding patterns of fMRI brain activations can be learned. They have demonstrated that such a mapping for concrete nouns is able to predict brain activations with accuracy levels significantly above chance, but the more recent elaborations have achieved relatively little performance improvement over the original study. In fact, the absolute accuracies of all these models are still currently rather limited, and it is not clear which aspects of the approach need improving in order to achieve performance levels that might lead to better accounts of human capabilities. This paper presents a systematic series of computational experiments designed to identify the limiting factors of the approach. Two distinct series of artificial brain activation vectors with varying levels of noise are introduced to characterize how the brain activation data restricts performance, and improved corpus based semantic vectors are developed to determine how the word set and model inputs affect the results. These experiments lead to the conclusion that the current state-of-the-art input semantic representations are already operating nearly perfectly (at least for non-ambiguous concrete nouns), and that it is primarily the quality of the fMRI data that is limiting what can be achieved with this approach. The results allow the study to end with empirically informed suggestions about the best directions for future research in this area.
    PLoS ONE 01/2013; 8(3):e57191.
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    ABSTRACT: Direct touch manipulation interactions with technology are now commonplace and significant interest is building around their use in the culture and heritage domain. Such interactions can give people the opportunity to explore materials and artefacts in ways that would otherwise be unavailable. These are often heavily annotated and can be linked to a large array of related digital content, thus enriching the experience for the user. Research has addressed issues of how to present digital documents and their related annotations but at present it is unclear what the optimal interaction approach to navigating these annotations in a touch display context might be. In this paper we investigate the role of two alternative approaches to support the navigation of annotations in digitised documents in the context of a touch interface. Through a control study we demonstrate that, whilst the navigation paradigm displays a significant interaction with the type of annotations task performed, there is no discernible advantage of using a natural visual metaphor for annotation in this context. This suggests that design of digital document annotation navigation tools should account for the context and navigation tasks being considered.
    International Journal of Human-Computer Studies 01/2013; 71(12):1103–1111.
  • School of Computer Science, University of Birmingham, 01/2013, Degree: PhD
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