2,113 research outputs found

    Pareto-Path Multi-Task Multiple Kernel Learning

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    A traditional and intuitively appealing Multi-Task Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing amongst tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a Multi-Objective Optimization (MOO) problem, which considers the concurrent optimization of all task objectives involved in the Multi-Task Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel Support Vector Machine (SVM) MT-MKL framework, that considers an implicitly-defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving better classification performance, when compared to other similar MTL approaches.Comment: Accepted by IEEE Transactions on Neural Networks and Learning System

    The Concept of Time in 2D Quantum Gravity

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    We show that the ``time'' t_s defined via spin clusters in the Ising model coupled to 2d gravity leads to a fractal dimension d_h(s) = 6 of space-time at the critical point, as advocated by Ishibashi and Kawai. In the unmagnetized phase, however, this definition of Hausdorff dimension breaks down. Numerical measurements are consistent with these results. The same definition leads to d_h(s)=16 at the critical point when applied to flat space. The fractal dimension d_h(s) is in disagreement with both analytical prediction and numerical determination of the fractal dimension d_h(g), which is based on the use of the geodesic distance t_g as ``proper time''. There seems to be no simple relation of the kind t_s = t_g^{d_h(g)/d_h(s)}, as expected by dimensional reasons.Comment: 14 pages, LaTeX, 2 ps-figure

    Random projection to preserve patient privacy

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    With the availability of accessible and widely used cloud services, it is natural that large components of healthcare systems migrate to them; for example, patient databases can be stored and processed in the cloud. Such cloud services provide enhanced flexibility and additional gains, such as availability, ease of data share, and so on. This trend poses serious threats regarding the privacy of the patients and the trust that an individual must put into the healthcare system itself. Thus, there is a strong need of privacy preservation, achieved through a variety of different approaches. In this paper, we study the application of a random projection-based approach to patient data as a means to achieve two goals: (1) provably mask the identity of users under some adversarial-attack settings, (2) preserve enough information to allow for aggregate data analysis and application of machine-learning techniques. As far as we know, such approaches have not been applied and tested on medical data. We analyze the tradeoff between the loss of accuracy on the outcome of machine-learning algorithms and the resilience against an adversary. We show that random projections proved to be strong against known input/output attacks while offering high quality data, as long as the projected space is smaller than the original space, and as long as the amount of leaked data available to the adversary is limited

    Mapping the First 10 Years with Leximancer: Themes and Concepts in the Sports Management International Journal Choregia

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    This study uses Leximancer (a text-mining tool for visualizing the structure of concepts and themes in text) to map the published research within Sports Management International Journal Choregia from 2005 to 2014. Drawing on 88 papers, of which 61 were classified as empirical and 27 as non-empirical ones, results reveal that the last half of the examined period concerned works that do not relate to the Greek context, which has been the case during the first years of Choregia’s publication. ‘Sports participation’, ‘physical activity’, ‘Greek football clubs’ – all largely associated with ‘management’ and ‘factors’ – shape the main themes in the studies published within Choregia. In addition, an emphasis on positivistic approaches, through the employment of questionnaires and utilizing students as the population for data collection, appears to be the dominant methodological orientation of the published content in Choregia. Becoming the platform for studies that originate beyond the American, Greek, and Iranian contexts, through special issues and invited contributions in the form of research notes would potentially increase this outlet’s scope and depth (that is, context and themes, respectively)
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