C
Cheolwoo Park
Researcher at KAIST
Publications - 67
Citations - 1495
Cheolwoo Park is an academic researcher from KAIST. The author has contributed to research in topics: Estimator & Wavelet. The author has an hindex of 20, co-authored 66 publications receiving 1395 citations. Previous affiliations of Cheolwoo Park include National Institutes of Health & Statistical and Applied Mathematical Sciences Institute.
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Gene selection using support vector machines with non-convex penalty
TL;DR: A unified procedure for simultaneous gene selection and cancer classification is provided, achieving high accuracy in both aspects and a successive quadratic algorithm is proposed to convert the non-differentiable and non-convex optimization problem into easily solved linear equation systems.
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On the wavelet spectrum diagnostic for Hurst parameter estimation in the analysis of Internet traffic
TL;DR: This work explores the use of the wavelet spectrum, whose slope is commonly used to estimate the Hurst parameter of long-range dependence, and shows that much more than simple slope estimates are needed for detecting important traffic features.
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Support vector machines with adaptive Lq penalty
TL;DR: This article proposes and explores an adaptive learning procedure called the L"q SVM, where the best q>0 is automatically chosen by data, and shows that the new adaptive approach combines the benefit of a class of non-adaptive procedures and gives the best performance of this class across a variety of situations.
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LASS: a tool for the local analysis of self-similarity
TL;DR: This work focuses on practical issues associated with the detection of long-range dependence in Internet traffic data and proposes two tools that can be used to address some of these issues and proposes a statistical tool for the local analysis of self-similarity (LASS).
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Long-range dependence in a changing Internet traffic mix
TL;DR: A deep analysis of long-range dependence in a continually evolving Internet traffic mix by employing a number of recently developed statistical methods and found and analyzed several of the time series that exhibited more "bursty" characteristics than could be modeled as fractional Gaussian noise.