Kyungha Seok

Support Vector Quantile Regression Using Asymmetric e-Insensitive Loss Function

Support vector quantile regression(SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse SVQR to overcome a limitation of SVQR, nonsparsity. The asymmetric e-insensitive loss function is used to efficiently provide sparsity. The experimental results are presented to illustrate the performance of the proposed method by comparing it with nonsparse SVQR.

Cytokinesis of the binucleate zoosporangia of Allomyces macrogynus.

Allomyces macrogynus, a true fungus, produces zoosporangia which discharge uninucleate zoospores after cytoplasmic cleavage. Binucleate zoosporangia of A. macrogynus were induced and examined to understand the basic principles of cytokinesis associated with the multinucleate zoosporangia. Development of cleavage membranes was visualized by constructing three dimensional models based on electron micrographs and confocal images. Cleavage membranes on the cleavage plane showed asymmetric ingression from the cortex, but cleavage of cytoplasm was completed by the fusion of cleavage membranes with plasma membrane. Also, the position of the cleavage plane was continuously rotated until settled at the last stage. These studies suggest that the positions of the numerous cleavage planes within a multinucleate zoosporangium are continuously adjusted during development of cleavage membranes. The final settlement of cleavage planes would define the exact boundary of cleavage planes and the expansion of cleavage membranes toward the boundary could complete the cleavage of cytoplasm.

Kernel Ridge Regression with Randomly Right Censored Data

This paper deals with the estimations of kernel ridge regression when the responses are subject to randomly right censoring. The iterative reweighted least squares(IRWLS) procedure is employed to treat censored observations. The hyperparameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation(GCV) function. Experimental results are then presented which indicate the performance of the proposed procedure.

Monotone support vector quantile regression

ABSTRACT Quantile regression (QR) models have received a great deal of attention in both the theoretical and applied statistical literature. In this paper we propose support vector quantile regression (SVQR) with monotonicity restriction, which is easily obtained via the dual formulation of the optimization problem. We also provide the generalized approximate cross validation method for choosing the hyperparameters which affect the performance of the proposed SVQR. The experimental results for the synthetic and real data sets confirm the successful performance of the proposed model.

Hybrid Fuzzy Least Squares Support Vector Machine Regression for Crisp Input and Fuzzy Output

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.