Gyeongyong Heo

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A variety of algorithms are presented and employed in a hierarchical fashion to discriminate both Anti-Tank (AT) and Anti-Personnel (AP) landmines using data collected from Wideband ElectroMagnetic Induction (WEMI) and Ground Penetrating Radar (GPR) sensors mounted on a robotic platform. The two new algorithms for WEMI are based on the In-phase vs.(More)
This paper addresses the problem of estimating the correct number of components in a Gaussian mixture given a sample data set. In particular, an extension of Gaussian-means (G-means) and Projected Gaussian-means (PG-means) algorithms is proposed. All these methods are based on one-dimensional statistical hypothesis test. G-means and PG-means are wrapper(More)
Principal component analysis (PCA) is widely used for dimensionality reduction in pattern recognition. Although PCA has been applied in many areas successfully, it suffers from sensitivity to noise and is limited to linear principal components. The noise sensitivity problem comes from the least-squares measure used in PCA and the limitation to linear(More)
— The support vector machine (SVM) of statistical learning theory was successfully applied in various fields, but still suffers from noise sensitivity originating from the fact that all the data points are treated equally. To relax this problem, the SVM was extended into a fuzzy SVM (FSVM) by the introduction of fuzzy memberships. The FSVM also has been(More)
Principal component analysis (PCA) is a mathematical method that reduces the dimensionality of the data while retaining most of the variation in the data. Although PCA has been applied in many areas successfully, it suffers from sensitivity to noise and is limited to linear principal components. The noise sensitivity problem comes from the least-squares(More)