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Least Square-Support Vector Regression (LS-SVR)

Particle swarm optimized partial least square support vector regression model for tax revenue prediction

Particle swarm optimized partial least square support vector regression model for tax revenue prediction

... of support vector regression(SVR) construct the optimal regression function through risk minimization principle to transform the problem into solving a convex quadratic programming ...data ...

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Runway Incursion Event Forecast Model based on LS-SVR with Multi-kernel

Runway Incursion Event Forecast Model based on LS-SVR with Multi-kernel

... machine, support vector machine (SVM) has been gaining popularity due to their promising performance, such as dealing with the data of small sample, the high dimension and the excellent generalization ...

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Development Of An Adaptive Soft Sensor Based On FCMILSSVR

Development Of An Adaptive Soft Sensor Based On FCMILSSVR

... of Least Square Support Vector Regression (LSSVR) with Fuzzy C-Means (FCM) clustering is proposed in this ...online Support Vector Regression (SVR) ...

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The Application of Least Square Support Vector Machine as a Mathematical Algorithm for Diagnosing Drilling Effectivity in Shaly Formations

The Application of Least Square Support Vector Machine as a Mathematical Algorithm for Diagnosing Drilling Effectivity in Shaly Formations

... robust support vector regression (SVR) methodology that offers superior performance for important drilling engineering ...hybrid least square support vector ...

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Hybrid Simulation of a Frame Equipped with MR Damper by Utilizing Least Square Support Vector Machine

Hybrid Simulation of a Frame Equipped with MR Damper by Utilizing Least Square Support Vector Machine

... 2. Least square support vector machine (LS-SVM) Supervised learning systems that analyze data and recognize patterns, known as support vector machines (SVMs), are used for ...

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Self organizing map and least square support vector machine method for river flow modelling

Self organizing map and least square support vector machine method for river flow modelling

... called Least Square Support Vector Machine (LSSVM) to encounter the SVM quadratic programming ...the least squares linear system as its loss function, which is computationally ...and ...

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Modeling of Corrosion-Fatigue Crack Growth Rate Based on Least Square Support Vector Machine Technique

Modeling of Corrosion-Fatigue Crack Growth Rate Based on Least Square Support Vector Machine Technique

... radial basis function network and genetic algorithms to optimize the back propagation network for the fatigue crack growth calculations. The results indicate that the applied model can successfully predict the ...

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A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

... To illustrate the capability of the SOM-LSSVM model, monthly river flow data from Bernam River were analyze in this study. The river flow data were varied per the number of input data used in the experiments. The number ...

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A Hybrid Method of Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm for Medium Term Electricity Price Forecasting

A Hybrid Method of Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm for Medium Term Electricity Price Forecasting

... estimation. Support Vector Regression solves for quadratic programs which involve inequality ...Hence, Least Squares Support Vector Machine (LSSVM) was proposed to diminish the ...

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Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

... As data-driven models rely on predictive features in historical data to forecast future drought, climate indices and sea surface temperature (SST) were used as regression covariates to feed in such attributes and ...

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Quantitative Structure Property Relationships Study of Mobility of Some Benzoaromatic Carboxylate Derivatives by Partial Least Squares and Least-Square Support Vector Machine

Quantitative Structure Property Relationships Study of Mobility of Some Benzoaromatic Carboxylate Derivatives by Partial Least Squares and Least-Square Support Vector Machine

... the least squares support vector machines ...mean square error of prediction (RMSEP) of ...linear regression (MLR) and partial least squares ...

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Multi-Output Least Square Support Vector Machine for the Reconstruction of Perfect Electric Conductor Below Rough Surface

Multi-Output Least Square Support Vector Machine for the Reconstruction of Perfect Electric Conductor Below Rough Surface

... a regression problem and solves it using learning by examples ...application. Support vector machine (SVM) [9], another learning by example method, can avoid these shortcomings of ANN by solving a ...

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Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model

Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model

... centre vector and diagonal covariance ...Mean Square Eerror ...centre vector and diagonal covariance matrix is performed using a guided random search algorithm called the Repeated Weighted Boosting ...

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Aircraft taxi time prediction: comparisons and insights

Aircraft taxi time prediction: comparisons and insights

... erent regression approaches for Zurich ...linear regression approach (LinReg) which is used as a baseline ...that least median square linear regression performs (LMS) poorly for ...

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Least Square Regression Learning with Data Dependent Hypothesis and Coefficient Regularzation

Least Square Regression Learning with Data Dependent Hypothesis and Coefficient Regularzation

... where Ω ( ) α is a positive function on ℜ m . Formulation (3) is a data dependent scheme which has been found many applications in the design of support vector machines, micro-array analysis and variable ...

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Study On Prediction Models For Integrated Scheduling In Semiconductor Manufacturing Lines

Study On Prediction Models For Integrated Scheduling In Semiconductor Manufacturing Lines

... Extreme Least Square Support Vector Machine for quality prediction and integrated scheduling of semiconductor production line is presented, in which data is first mapped into ELM feature space ...

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Diabetes Prediction using Linear Regression, Decision Tree & Least Square Support Vector Machine

Diabetes Prediction using Linear Regression, Decision Tree & Least Square Support Vector Machine

... Confusion Matrix pertaining set of predictors among which some are significant in terms of mean and variance than others where estimating the predictors if the same significant predictors before prediction, pose as ...

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Least square support vector and multi-linear regression for statistically downscaling general circulation model outputs to catchment streamflows

Least square support vector and multi-linear regression for statistically downscaling general circulation model outputs to catchment streamflows

... The major assumption in statistical downscaling is that the predictor–predictand relationships derived during the model development stage are valid for the future climatic conditions (Wilby and Wigly, 2000). Benestad et ...

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Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models

Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models

... GARCH (General Autoregressive Conditional Heteroskedasticity) by Bollerslev is a linear time series prediction method. It is a standard textbook material in econometrics and finance[6]. There are many families of GARCH ...

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Orthogonal Least Square with Boosting for Regression

Orthogonal Least Square with Boosting for Regression

... orthogonal least square (OLS) algorithm [1]–[4] is popular for nonlinear data mod- eling practicians, for the reason that the algorithm is simple and efficient, and is capable of producing parsimonious ...

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