Integration of CMAC-GBF and Support Vector Regression Techniques

2007 ◽  
Author(s):  
Chen-Chia Chuang ◽  
Chia-Chu Hsu ◽  
Jin-Tsong Jeng
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Regression Techniques

scholarly journals Improved Direction-of-Arrival Estimation of an Acoustic Source Using Support Vector Regression and Signal Correlation

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2692
Author(s):  
Faisal Alam ◽  
Mohammed Usman ◽  
Hend I. Alkhammash ◽  
Mohd Wajid
Keyword(s):  
Support Vector Regression ◽  
Cross Correlation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Regression Technique ◽  
Support Vector ◽  
Correlation Technique ◽  
Multilevel Regression ◽  
Acoustic Source ◽  
Regression Techniques

The direction-of-arrival (DoA) estimation of an acoustic source can be estimated with a uniform linear array using classical techniques such as generalized cross-correlation, beamforming, subspace techniques, etc. However, these methods require a search in the angular space and also have a higher angular error at the end-fire. In this paper, we propose the use of regression techniques to improve the results of DoA estimation at all angles including the end-fire. The proposed methodology employs curve-fitting on the received multi-channel microphone signals, which when applied in tandem with support vector regression (SVR) provides a better estimation of DoA as compared to the conventional techniques and other polynomial regression techniques. A multilevel regression technique is also proposed, which further improves the estimation accuracy at the end-fire. This multilevel regression technique employs the use of linear regression over the results obtained from SVR. The techniques employed here yielded an overall 63% improvement over the classical generalized cross-correlation technique.

scholarly journals Affinity Prediction using Mutated Protein-Ligand Docking with Regression Techniques of SCA

2019 ◽  
Vol 8 (2) ◽  
pp. 3642-3648
Keyword(s):  
Drug Design ◽  
Support Vector Regression ◽  
Spinocerebellar Ataxia ◽  
Genetic Disorder ◽  
Ligand Docking ◽  
Support Vector ◽  
Autodock Vina ◽  
Affinity Prediction ◽  
Energy Profiles ◽  
Regression Techniques

Drug discovery for rare genetic disorder like spinocerebellar ataxia is very complicated in biomedical research. Numerous approaches are available for drug design in clinical labs, but it is time consuming. There is a need for affinity prediction of spinocerebellar ataxia, which will help in facilitating the drug design. In this work, the proteins are mutated with the information available from HGMD database. The repeat mutations are induced manually, and that mutated proteins are docked with ligand. The model is trained with extricated features such as energy profiles, rf-score, autodock vina scores, cyscore and sequence descriptors. Regression techniques like linear, polynomial, ridge, SVM and neural network regression are implemented. The predictive models are built with various regression techniques and the predictive model implemented with support vector regression is compared with support vector regression kernel. Among all regression techniques, SVR performs well than the other regression models.

Evaluating trends of airborne contaminants by using support vector regression techniques

2011 ◽  
Author(s):  
Artemio Sotomayor-Olmedo ◽  
M. Antonio Aceves-Fernandez ◽  
Efren Gorrostieta-Hurtado ◽  
J. Carlos Pedraza-Ortega ◽  
J. Emilio Vargas-Soto ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Airborne Contaminants ◽  
Regression Techniques

scholarly journals Using genetic algorithms to improve support vector regression in the analysis of atomic spectra of lubricant oils

2016 ◽  
Vol 33 (4) ◽  
pp. 995-1005 ◽  
Author(s):  
Carlos Fernandez-Lozano ◽  
Francisco Cedrón ◽  
Daniel Rivero ◽  
Julian Dorado ◽  
José Manuel Andrade-Garda ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Regression ◽  
Spectroscopic Method ◽  
Chemical Properties ◽  
Global Solutions ◽  
Support Vector ◽  
Content Type ◽  
Regression Techniques ◽  
Calibration Samples

Purpose – The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate multiple regression, principal components analysis, partial least squares, and support vector regression (SVR)). Design/methodology/approach – The rationale behind the use of SVR was the fuzzy characteristics of the signal and its inherent ability to find nonlinear, global solutions in highly complex dimensional input spaces. Thus, SVR allows extracting useful information from calibration samples that makes it possible to characterize physical-chemical properties of the lubricant oils. Findings – A dataset of 42 spectra measured from oil standards was studied to assess the concentration of copper into the oils and, thus, evaluate the wearing of the machinery. It was found that the use of SVR was very advantageous to get a regression model. Originality/value – The use of genetic algorithms coupled to SVR was considered in order to reduce the time needed to find the optimal parameters required to get a suitable prediction model.

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