746 resultados para Japanese, impoliteness, online communities, BBS


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In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.

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We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated with the chosen assignment, and the goal is to minimize the cumulative loss of these choices relative to the best map on the entire sequence. Even though the offline problem of finding the best map is provably hard, we show that there is an equivalent online approximation algorithm, Randomized Map Prediction (RMP), that is efficient and performs nearly as well. While drawing upon results from the "Online Prediction with Expert Advice" setting, we show how RMP can be utilized as an online approach to several standard batch problems. We apply RMP to online clustering as well as online feature selection and, surprisingly, RMP often outperforms the standard batch algorithms on these problems.

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Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data analysis problems. In this paper, we analyze a class of online learning algorithms based on fixed potentials and nonlinearized losses, which yields algorithms with implicit update rules. We show how to efficiently compute these updates, and we prove regret bounds for the algorithms. We apply our formulation to several special cases where our approach has benefits over existing online learning methods. In particular, we provide improved algorithms and bounds for the online metric learning problem, and show improved robustness for online linear prediction problems. Results over a variety of data sets demonstrate the advantages of our framework.

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A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a prediction x from a convex set, the environment plays a loss function f, and the learner’s long-term goal is to minimize regret. Algorithms have been proposed by Zinkevich, when f is assumed to be convex, and Hazan et al., when f is assumed to be strongly convex, that have provably low regret. We consider these two settings and analyze such games from a minimax perspective, proving minimax strategies and lower bounds in each case. These results prove that the existing algorithms are essentially optimal.

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In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.

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We demonstrate a modification of the algorithm of Dani et al for the online linear optimization problem in the bandit setting, which allows us to achieve an O( \sqrt{T ln T} ) regret bound in high probability against an adaptive adversary, as opposed to the in expectation result against an oblivious adversary of Dani et al. We obtain the same dependence on the dimension as that exhibited by Dani et al. The results of this paper rest firmly on those of Dani et al and the remarkable technique of Auer et al for obtaining high-probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.

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We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

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My thesis consists of a creative work plus an exegesis. This exegesis uses case study research to investigate three Brisbane-based media organisations and the role they play in encouraging social inclusion and other positive social change for specific disadvantaged and stigmatised minority groups. Bailey, Cammaerts and Carpentier’s theoretical approach to alternative media forms the basis of this research. Bailey et al. (2008, p. 156) view alternative media organisations as having four important roles, two media-centred and two society-centred, which must all be considered to best understand them: • serving their communities • acting as an alternative to mainstream media discourses • promoting and advocating democratisation in the media and through the media in society • functioning as a crossroads in civil society. The first case study, about community radio station 4RPH (Radio for the Print Handicapped), centres on promoting social inclusion for people with a print disability through access to printed materials (primarily mainstream print media) in an audio format. The station also provides important opportunities for members of this group to produce media and, to a lesser extent, provides disability-specific information and discussions. The second case study, about gay print and online magazine Queensland Pride, focuses on promoting social inclusion and combating the discrimination and repression of people who identify as lesbian, gay, bisexual or transgender. Central issues include the representation (including sexualised representation) of a subculture and niche target market, and the impact of commercialisation on this free publication. The third case study, about community radio station 98.9FM, explores the promotion of social inclusion for peoples whose identity, cultures, issues, politics and contributions are often absent or misrepresented in the mainstream media. This radio station provides “a first level of service” (Meadows & van Vuuren, 1998, p. 104) to these people, but also informs and entertains those in the majority society. The findings of this research suggest that there are two key mechanisms that help these media organisations to effect social change: first, strengthening the minority community and serving its needs, and second, fostering connections with the broader society.