The Experts below are selected from a list of 143601 Experts worldwide ranked by ideXlab platform
Keshab K Parhi - One of the best experts on this subject based on the ideXlab platform.
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computing radial basis function support vector machine using dna via fractional coding
Design Automation Conference, 2019Co-Authors: Xingyi Liu, Keshab K ParhiAbstract:This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format conversion. Two designs are presented; one is based on the explicit and the other is based on implicit conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format conversion has orders of magnitude less Regression Error than that based on implicit conversion.
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computing radial basis function support vector machine using dna via fractional coding
Design Automation Conference, 2019Co-Authors: Xingyi Liu, Keshab K ParhiAbstract:This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format conversion. Two designs are presented; one is based on the explicit and the other is based on implicit conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format conversion has orders of magnitude less Regression Error than that based on implicit conversion. CCS CONCEPTS • Applied computing → Molecular structural biology; •Hardware → Biology-related information processing;
Lefteris Angelis - One of the best experts on this subject based on the ideXlab platform.
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a permutation test based on Regression Error characteristic curves for software cost estimation models
Empirical Software Engineering, 2012Co-Authors: Nikolaos Mittas, Lefteris AngelisAbstract:Background Regression Error Characteristic (REC) curves provide a visualization tool, able to characterize graphically the prediction power of alternative predictive models. Due to the benefits of using such a visualization description of the whole distribution of Error, REC analysis was recently introduced in software cost estimation to aid the decision of choosing the most appropriate cost estimation model during the management of a forthcoming project.
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LSEbA: least squares Regression and estimation by analogy in a semi-parametric model for software cost estimation
Empirical Software Engineering, 2010Co-Authors: Nikolaos Mittas, Lefteris AngelisAbstract:The importance of Software Cost Estimation at the early stages of the development life cycle is clearly portrayed by the utilization of several models and methods, appeared so far in the literature. The researchers’ interest has been focused on two well known techniques, namely the parametric Regression Analysis and the non-parametric Estimation by Analogy. Despite the several comparison studies, there seems to be a discrepancy in choosing the best prediction technique between them. In this paper, we introduce a semi-parametric technique, called LSEbA that achieves to combine the aforementioned methods retaining the advantages of both approaches. Furthermore, the proposed method is consistent with the mixed nature of Software Cost Estimation data and takes advantage of the whole pure information of the dataset even if there is a large amount of missing values. The paper analytically illustrates the process of building such a model and presents the experimentation on three representative datasets verifying the benefits of the proposed model in terms of accuracy, bias and spread. Comparisons of LSEbA with linear Regression, estimation by analogy and a combination of them, based on the average of their outcomes are made through accuracy metrics, statistical tests and a graphical tool, the Regression Error Characteristic curves.
Xingyi Liu - One of the best experts on this subject based on the ideXlab platform.
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computing radial basis function support vector machine using dna via fractional coding
Design Automation Conference, 2019Co-Authors: Xingyi Liu, Keshab K ParhiAbstract:This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format conversion. Two designs are presented; one is based on the explicit and the other is based on implicit conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format conversion has orders of magnitude less Regression Error than that based on implicit conversion.
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computing radial basis function support vector machine using dna via fractional coding
Design Automation Conference, 2019Co-Authors: Xingyi Liu, Keshab K ParhiAbstract:This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format conversion. Two designs are presented; one is based on the explicit and the other is based on implicit conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format conversion has orders of magnitude less Regression Error than that based on implicit conversion. CCS CONCEPTS • Applied computing → Molecular structural biology; •Hardware → Biology-related information processing;
Nikolaos Mittas - One of the best experts on this subject based on the ideXlab platform.
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a permutation test based on Regression Error characteristic curves for software cost estimation models
Empirical Software Engineering, 2012Co-Authors: Nikolaos Mittas, Lefteris AngelisAbstract:Background Regression Error Characteristic (REC) curves provide a visualization tool, able to characterize graphically the prediction power of alternative predictive models. Due to the benefits of using such a visualization description of the whole distribution of Error, REC analysis was recently introduced in software cost estimation to aid the decision of choosing the most appropriate cost estimation model during the management of a forthcoming project.
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LSEbA: least squares Regression and estimation by analogy in a semi-parametric model for software cost estimation
Empirical Software Engineering, 2010Co-Authors: Nikolaos Mittas, Lefteris AngelisAbstract:The importance of Software Cost Estimation at the early stages of the development life cycle is clearly portrayed by the utilization of several models and methods, appeared so far in the literature. The researchers’ interest has been focused on two well known techniques, namely the parametric Regression Analysis and the non-parametric Estimation by Analogy. Despite the several comparison studies, there seems to be a discrepancy in choosing the best prediction technique between them. In this paper, we introduce a semi-parametric technique, called LSEbA that achieves to combine the aforementioned methods retaining the advantages of both approaches. Furthermore, the proposed method is consistent with the mixed nature of Software Cost Estimation data and takes advantage of the whole pure information of the dataset even if there is a large amount of missing values. The paper analytically illustrates the process of building such a model and presents the experimentation on three representative datasets verifying the benefits of the proposed model in terms of accuracy, bias and spread. Comparisons of LSEbA with linear Regression, estimation by analogy and a combination of them, based on the average of their outcomes are made through accuracy metrics, statistical tests and a graphical tool, the Regression Error Characteristic curves.
Lixing Zhu - One of the best experts on this subject based on the ideXlab platform.
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a lack of fit test for quantile Regression
Journal of the American Statistical Association, 2003Co-Authors: Lixing ZhuAbstract:We propose an omnibus lack-of-fit test for linear or nonlinear quantile Regression based on a cusum process of the gradient vector. The test does not involve nonparametric smoothing but is consistent for all nonparametric alternatives without any moment conditions on the Regression Error. In addition, the test is suitable for detecting the local alternatives of any order arbitrarily close to n−1/2 from the null hypothesis. The limiting distribution of the proposed test statistic is non-Gaussian but can be characterized by a Gaussian process. We propose a simple sequential resampling scheme to carry out the test whose nominal levels are well approximated in our empirical study for
