H
Hassan A. Kingravi
Researcher at Georgia Institute of Technology
Publications - 44
Citations - 2703
Hassan A. Kingravi is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Adaptive control & Gaussian process. The author has an hindex of 17, co-authored 43 publications receiving 2280 citations. Previous affiliations of Hassan A. Kingravi include Oklahoma State University–Stillwater & Texas A&M University.
Papers
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A comparative study of efficient initialization methods for the k-means clustering algorithm
TL;DR: It is demonstrated that popular initialization methods often perform poorly and that there are in fact strong alternatives to these methods, and eight commonly used linear time complexity initialization methods are compared.
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A Methodological Approach to the Classification of Dermoscopy Images
M. Emre Celebi,Hassan A. Kingravi,Bakhtiyar Uddin,Hitoshi Iyatomi,Y. Alp Aslandogan,William V. Stoecker,Randy Hays Moss +6 more
TL;DR: A methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented and the issue of class imbalance is addressed using various sampling strategies and the classifier generalization error is estimated using Monte Carlo cross validation.
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Border detection in dermoscopy images using statistical region merging.
M. Emre Celebi,Hassan A. Kingravi,Hitoshi Iyatomi,Y. Alp Aslandogan,William V. Stoecker,Randy Hays Moss,Joseph M. Malters,James M. Grichnik,Ashfaq A. Marghoob,Harold S. Rabinovitz,Scott W. Menzies +10 more
TL;DR: This work has shown that automated border detection is one of the most important steps in the computer‐aided diagnosis of melanoma, because the accuracy of the subsequent steps crucially depends on it.
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Bayesian Nonparametric Adaptive Control Using Gaussian Processes
TL;DR: This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions.
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Nonlinear Vector Filtering for Impulsive Noise Removal from Color Images
TL;DR: A comprehensive survey of 48 filters for impulsive noise removal from color images is presented and suggestions are provided on how to choose a filter given certain requirements.