1,721,343 research outputs found
Existence of Solutions to Wasserstein Gradient Flows and Their Long Time Asymptotic Behaviors
No full textNg, Wing Kit.Thesis M.Phil. Chinese University of Hong Kong 2016.Includes bibliographical references (leaves ).Abstracts also in Chinese.Title from PDF title page (viewed on …)
An assessment of buyer satisfactory of a multinational air conditioning company's service division.
Full text linkby Ng Wing Yiu.Thesis (M.B.A.)--Chinese University of Hong Kong.Bibliography: leaves 75-76
A study of the market potential for coal in Asia : research report.
No full textby Ng Wing-hong.Abstract also in ChineseBibliography: leaf 91Thesis (M.B.A.)--Chinese University of Hong Kong, 198
CUHK electronic theses & dissertations collection
No full textNg, Wing Fung.Thesis Ph.D. Chinese University of Hong Kong 2015.Includes bibliographical references (leaves 109-124).Abstracts also in Chinese.Title from PDF title page (viewed on 31, October, 2016)
On the performance of oscillators on G7 stock market indices.
Full text linkNg Wing-kam.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 54-55).Abstracts in English and Chinese.Chapter ONE --- INTRODUCTION --- p.1Chapter TWO --- DATA AND TECHNICAL TRADING RULES --- p.4DataTechnical Trading RulesRSIMACDChapter THREE --- EMPIRICAL RESULTS --- p.10Sample StatisticsTechnical Trading Rules (Without Transaction Cost)MACDRSITechnical Trading Rules (With Transaction Cost)MACDRSIChapter FOUR --- CONCLUSION --- p.37TABLES --- p.40BIBLOGRAPHY --- p.5
LiSSA: Localized Stochastic Sensitive Autoencoders
No full textThe training of autoencoder (AE) focuses on the selection of connection weights via a minimization of both the training error and a regularized term. However, the ultimate goal of AE training is to autoencode future unseen samples correctly (i.e., good generalization). Minimizing the training error with different regularized terms only indirectly minimizes the generalization error. Moreover, the trained model may not be robust to small perturbations of inputs which may lead to a poor generalization capability. In this paper, we propose a localized stochastic sensitive AE (LiSSA) to enhance the robustness of AE with respect to input perturbations. With the local stochastic sensitivity regularization, LiSSA reduces sensitivity to unseen samples with small differences (perturbations) from training samples. Meanwhile, LiSSA preserves the local connectivity from the original input space to the representation space that learns a more robustness features (intermediate representation) for unseen samples. The classifier using these learned features yields a better generalization capability. Extensive experimental results on 36 benchmarking datasets indicate that LiSSA outperforms several classical and recent AE training methods significantly on classification tasks
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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