The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform
Jooyong Shim - One of the best experts on this subject based on the ideXlab platform.
-
svqr with asymmetric quadratic loss Function
Journal of the Korean Data and Information Science Society, 2015Co-Authors: Jooyong Shim, Kyungha SeokAbstract:Support vector quantile regression (SVQR) can be obtained by applying support vector machine with a check Function instead of an e-insensitive loss Function into the quantile regression, which still requires to solve a quadratic program (QP) problem which is time and memory expensive. In this paper we propose an SVQR whose objective Function is composed of an asymmetric quadratic loss Function. The proposed method overcomes the weak point of the SVQR with the check Function. We use the iterative procedure to solve the objective problem. Furthermore, we introduce the generalized cross Validation Function to select the hyper-parameters which affect the performance of SVQR. Experimental results are then presented, which illustrate the performance of proposed SVQR.
-
Composite support vector quantile regression estimation
Computational Statistics, 2014Co-Authors: Jooyong Shim, Changha Hwang, Kyungha SeokAbstract:In this paper we propose a new nonparametric regression method called composite support vector quantile regression (CSVQR) that combines the formulations of support vector regression and composite quantile regression. First the CSVQR using the quadratic programming (QP) is proposed and then the CSVQR utilizing the iteratively reweighted least squares (IRWLS) procedure is proposed to overcome weakness of the QP based method in terms of computation time. The IRWLS procedure based method enables us to derive a generalized cross Validation (GCV) Function that is easier and faster than the conventional cross Validation Function. The GCV Function facilitates choosing the hyperparameters that affect the performance of the CSVQR and saving computation time. Numerical experiment results are presented to illustrate the performance of the proposed method
-
support vector quantile regression with weighted quadratic loss Function
Communications for Statistical Applications and Methods, 2010Co-Authors: Jooyong Shim, Changha HwangAbstract:Support vector quantile regression(SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the problem of SVQR with a weighted quadratic loss Function. Furthermore, we introduce the generalized approximate cross Validation Function to select the hyperparameters which affect the performance of SVQR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for SVQR.
-
ICNC (1) - Doubly regularized kernel regression with heteroscedastic censored data
Lecture Notes in Computer Science, 2005Co-Authors: Jooyong Shim, Changha HwangAbstract:A doubly regularized likelihood estimating procedure is introduced for the heteroscedastic censored regression. The proposed procedure provides the estimates of both the conditional mean and the variance of the response variables, which are obtained by two stepwise iterative fashion. The generalized cross Validation Function and the generalized approximate cross Validation Function are used alternately to estimate tuning parameters in each step. Experimental results are then presented which indicate the performance of the proposed estimating procedure.
Changha Hwang - One of the best experts on this subject based on the ideXlab platform.
-
Composite support vector quantile regression estimation
Computational Statistics, 2014Co-Authors: Jooyong Shim, Changha Hwang, Kyungha SeokAbstract:In this paper we propose a new nonparametric regression method called composite support vector quantile regression (CSVQR) that combines the formulations of support vector regression and composite quantile regression. First the CSVQR using the quadratic programming (QP) is proposed and then the CSVQR utilizing the iteratively reweighted least squares (IRWLS) procedure is proposed to overcome weakness of the QP based method in terms of computation time. The IRWLS procedure based method enables us to derive a generalized cross Validation (GCV) Function that is easier and faster than the conventional cross Validation Function. The GCV Function facilitates choosing the hyperparameters that affect the performance of the CSVQR and saving computation time. Numerical experiment results are presented to illustrate the performance of the proposed method
-
support vector quantile regression with weighted quadratic loss Function
Communications for Statistical Applications and Methods, 2010Co-Authors: Jooyong Shim, Changha HwangAbstract:Support vector quantile regression(SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the problem of SVQR with a weighted quadratic loss Function. Furthermore, we introduce the generalized approximate cross Validation Function to select the hyperparameters which affect the performance of SVQR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for SVQR.
-
ICNC (1) - Doubly regularized kernel regression with heteroscedastic censored data
Lecture Notes in Computer Science, 2005Co-Authors: Jooyong Shim, Changha HwangAbstract:A doubly regularized likelihood estimating procedure is introduced for the heteroscedastic censored regression. The proposed procedure provides the estimates of both the conditional mean and the variance of the response variables, which are obtained by two stepwise iterative fashion. The generalized cross Validation Function and the generalized approximate cross Validation Function are used alternately to estimate tuning parameters in each step. Experimental results are then presented which indicate the performance of the proposed estimating procedure.
Kyungha Seok - One of the best experts on this subject based on the ideXlab platform.
-
svqr with asymmetric quadratic loss Function
Journal of the Korean Data and Information Science Society, 2015Co-Authors: Jooyong Shim, Kyungha SeokAbstract:Support vector quantile regression (SVQR) can be obtained by applying support vector machine with a check Function instead of an e-insensitive loss Function into the quantile regression, which still requires to solve a quadratic program (QP) problem which is time and memory expensive. In this paper we propose an SVQR whose objective Function is composed of an asymmetric quadratic loss Function. The proposed method overcomes the weak point of the SVQR with the check Function. We use the iterative procedure to solve the objective problem. Furthermore, we introduce the generalized cross Validation Function to select the hyper-parameters which affect the performance of SVQR. Experimental results are then presented, which illustrate the performance of proposed SVQR.
-
Composite support vector quantile regression estimation
Computational Statistics, 2014Co-Authors: Jooyong Shim, Changha Hwang, Kyungha SeokAbstract:In this paper we propose a new nonparametric regression method called composite support vector quantile regression (CSVQR) that combines the formulations of support vector regression and composite quantile regression. First the CSVQR using the quadratic programming (QP) is proposed and then the CSVQR utilizing the iteratively reweighted least squares (IRWLS) procedure is proposed to overcome weakness of the QP based method in terms of computation time. The IRWLS procedure based method enables us to derive a generalized cross Validation (GCV) Function that is easier and faster than the conventional cross Validation Function. The GCV Function facilitates choosing the hyperparameters that affect the performance of the CSVQR and saving computation time. Numerical experiment results are presented to illustrate the performance of the proposed method
Chong Jin Ong - One of the best experts on this subject based on the ideXlab platform.
-
Determination of Global Minima of Some Common Validation Functions in Support Vector Machine
IEEE transactions on neural networks, 2011Co-Authors: Jianbo Yang, Chong Jin OngAbstract:Tuning of the regularization parameter C is a well-known process in the implementation of a support vector machine (SVM) classifier. Such a tuning process uses an appropriate Validation Function whose value, evaluated over a Validation set, has to be optimized for the determination of the optimal C. Unfortunately, most common Validation Functions are not smooth Functions of C. This brief presents a method for obtaining the global optimal solution of these non-smooth Validation Functions. The method is guaranteed to find the global optimum and relies on the regularization solution path of SVM over a range of C values. When the solution path is available, the computation needed is minimal.
Jose L Gallegos - One of the best experts on this subject based on the ideXlab platform.
-
verifying client side input Validation Functions using string analysis
International Conference on Software Engineering, 2012Co-Authors: Muath Alkhalaf, Tevfik Bultan, Jose L GallegosAbstract:Client-side computation in web applications is becoming increasingly common due to the popularity of powerful client-side programming languages such as JavaScript. Client-side computation is commonly used to improve an application's responsiveness by validating user inputs before they are sent to the server. In this paper, we present an analysis technique for checking if a client-side input Validation Function conforms to a given policy. In our approach, input Validation policies are expressed using two regular expressions, one specifying the maximum policy (the upper bound for the set of inputs that should be allowed) and the other specifying the minimum policy (the lower bound for the set of inputs that should be allowed). Using our analysis we can identify two types of errors 1) the input Validation Function accepts an input that is not permitted by the maximum policy, or 2) the input Validation Function rejects an input that is permitted by the minimum policy. We implemented our analysis using dynamic slicing to automatically extract the input Validation Functions from web applications and using automata-based string analysis to analyze the extracted Functions. Our experiments demonstrate that our approach is effective in finding errors in input Validation Functions that we collected from real-world applications and from tutorials and books for teaching JavaScript.
-
ICSE - Verifying client-side input Validation Functions using string analysis
2012 34th International Conference on Software Engineering (ICSE), 2012Co-Authors: Muath Alkhalaf, Tevfik Bultan, Jose L GallegosAbstract:Client-side computation in web applications is becoming increasingly common due to the popularity of powerful client-side programming languages such as JavaScript. Client-side computation is commonly used to improve an application's responsiveness by validating user inputs before they are sent to the server. In this paper, we present an analysis technique for checking if a client-side input Validation Function conforms to a given policy. In our approach, input Validation policies are expressed using two regular expressions, one specifying the maximum policy (the upper bound for the set of inputs that should be allowed) and the other specifying the minimum policy (the lower bound for the set of inputs that should be allowed). Using our analysis we can identify two types of errors 1) the input Validation Function accepts an input that is not permitted by the maximum policy, or 2) the input Validation Function rejects an input that is permitted by the minimum policy. We implemented our analysis using dynamic slicing to automatically extract the input Validation Functions from web applications and using automata-based string analysis to analyze the extracted Functions. Our experiments demonstrate that our approach is effective in finding errors in input Validation Functions that we collected from real-world applications and from tutorials and books for teaching JavaScript.
