scholarly journals Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches

2021 ◽  
Author(s):  
Sandeep Samantaray ◽  
Dillip Kumar Ghose
Keyword(s):  
Physical Phenomenon ◽  
Support Vector ◽  
Data Sets ◽  
Hybrid Technique ◽  
Rainfall Runoff ◽  
Risk Minimization ◽  
Rainfall Simulator ◽  
Empirical Risk ◽  
Research Findings ◽  
Experimental Process

Abstract Effective prediction of runoff is a substantial feature for the successful management of hydrological phenomena in arid regions. The present research findings reveal that a rainfall simulator (RS) can be a valuable instrument to estimate runoff as the intensity of rainfall is modifiable in the course of an experimental process, which turns out to be of great advantage. Rainfall–runoff process is a complex physical phenomenon caused by the effect of various parameters. In this research, a new hybrid technique integrating PSR (phase space reconstruction) with FFA (firefly algorithm) and SVM (support vector machine) has gained recognition in various modelling investigations in contrast to the principle of empirical risk minimization through ANN practices. Outcomes of SVM are contrasted against SVM-FFA and PSR-SVM-FFA models. The improvements in NSE (Nash–Sutcliffe), RMSE (Root Mean Square Error), and WI (Willmott's Index) by PSR-SVM-FFA over SVM models specify that the prediction accuracy of the hybrid model is better. The established PSR-SVM-FFA model generates preeminent WI values that range from 0.97 to 0.98, while the SVM and SVM-FFA models encompass 0.93–0.95 and 0.96–0.97, respectively. The proposed PSR-SVM-FFA model gives more accurate results and error limiting up to 2–3%.

Empirical risk minimization for support vector classifiers

2003 ◽  
Vol 14 (2) ◽  
pp. 296-303 ◽  
Author(s):  
F. Perez-Cruz ◽  
A. Navia-Vazquez ◽  
A.R. Figueiras-Vidal ◽  
A. Artes-Rodriguez
Keyword(s):  
Support Vector ◽  
Empirical Risk Minimization ◽  
Risk Minimization ◽  
Empirical Risk

Constrained ERM Learning of Canonical Correlation Analysis: A Least Squares Perspective

2017 ◽  
Vol 29 (10) ◽  
pp. 2825-2859 ◽  
Author(s):  
Jia Cai ◽  
Hongwei Sun
Keyword(s):  
Correlation Analysis ◽  
Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Data Sets ◽  
Risk Minimization ◽  
Real World Data ◽  
Empirical Risk ◽  
Statistical Consistency ◽  
Kaczmarz Method

Canonical correlation analysis (CCA) is a useful tool in detecting the latent relationship between two sets of multivariate variables. In theoretical analysis of CCA, a regularization technique is utilized to investigate the consistency of its analysis. This letter addresses the consistency property of CCA from a least squares view. We construct a constrained empirical risk minimization framework of CCA and apply a two-stage randomized Kaczmarz method to solve it. In the first stage, we remove the noise, and in the second stage, we compute the canonical weight vectors. Rigorous theoretical consistency is addressed. The statistical consistency of this novel scenario is extended to the kernel version of it. Moreover, experiments on both synthetic and real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithms.

Modelling Concrete Strength Using Support Vector Machines

2013 ◽  
Vol 438-439 ◽  
pp. 170-173 ◽  
Author(s):  
Hai Ying Yang ◽  
Yi Feng Dong
Keyword(s):  
Compressive Strength ◽  
Support Vector Machines ◽  
Concrete Strength ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Data Sets ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Vector Machines ◽  
Compressive Strength Of Concrete

Support vector machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. A SVM model is presented to predict compressive strength of concrete at 28 days in this paper. A total of 20 data sets were used to train, whereas the remaining 10 data sets were used to test the created model. Radial basis function based on support vector machines was used to model the compressive strength and results were compared with a generalized regression neural network approach. The results of this study showed that the SVM approach has the potential to be a practical tool for predicting compressive strength of concrete at 28 days.

Research on Fault Diagnosis Based on AGA and LSSVM

2011 ◽  
Vol 383-390 ◽  
pp. 6938-6941
Author(s):  
Da Wei Zhang ◽  
Kai Zhang ◽  
Jing Jiang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Empirical Risk Minimization ◽  
Risk Minimization ◽  
Empirical Risk ◽  
Parameters Selection ◽  
Structural Risk

Support vector machine (SVM) has excellent learning, classification ability and generalization ability, which uses structural risk minimization instead of traditional empirical risk minimization based on large sample. The perfect performance of SVM will be realized only if the parameters are rightly selected. The accuracy and efficiency of classification largely depend on the quality of the parameters selection. Focusing on the problem of the parameters selection in least squares support vector machine (LSSVM), a new method is proposed to optimize the parameters in LSSVM using adaptive genetic algorithm. The research is provided using this method on the fault diagnosis of a certain type of helicopter’s helicopter-electrical-box. Simulated results show that the proposed method achieves perfect accuracy and efficiency in fault diagnosis.

CWT-Support Vector Regression Model and Its Application

2010 ◽  
Vol 113-116 ◽  
pp. 207-210
Author(s):  
Jie Fang Liu ◽  
Pu Mei Gao ◽  
Bao Lin Ma
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Support Vector ◽  
Traditional Learning ◽  
Risk Minimization ◽  
Support Vector Regression Model ◽  
Analytical Technique ◽  
Learning Machines ◽  
Empirical Risk ◽  
Svm Model

Near-infrared spectroscopy (NIR) analytical technique is simple, fast and low cost, making neither pollution nor damage to the samples, and can determine many components simultaneously. Continuous wavelet transform (CWT), as an application direction of the wavelet analysis, is keener to the signal slight change. Support vector machine (SVM) is based on the principle of structural risk minimization, which makes SVM has better generalization ability than other traditional learning machines that are based on the learning principle of empirical risk minimization. In this paper, we use CWT- SVM model to predict meat’s component. Compared with Partial Least Squares (PLS) and SVR, we get more satisfactory result.

Application of Supporting Vector Regression Model on Simultaneous Determination of Benzoic Acid and Salicylic Acid

2010 ◽  
Vol 26-28 ◽  
pp. 326-329
Author(s):  
Jie Fang Liu
Keyword(s):  
Salicylic Acid ◽  
Benzoic Acid ◽  
Support Vector ◽  
Traditional Learning ◽  
Risk Minimization ◽  
Learning Machines ◽  
Empirical Risk ◽  
Structural Risk ◽  
Highly Correlated

Support vector machine (SVM) is based on the principle of structural risk minimization, which makes SVM has better generalization ability than other traditional learning machines that are based on the learning principle of empirical risk minimization.Research on the application of Support vector regression （SVR） model in spectrophotometry was done to determine the content of benzoic acid and salicylic acid simultaneously. The predicted result was found highly correlated with the time when the data was collected to build the model. The closer of the dates between collecting data for modeling and for predicting, the better the predicted results. SVR model with significantly improved robustness was resulted by using all the collected data over time, which, when applied to the determination of benzoic acid and salicylic acid simultaneously, led to satisfactory result, with recoveries being 97%-102%.

scholarly journals Smooth twin support vector machines via unconstrained convex minimization

Filomat ◽  
2017 ◽  
Vol 31 (8) ◽  
pp. 2195-2210 ◽  
Author(s):  
M. Tanveer ◽  
K. Shubham
Keyword(s):  
Linear Equations ◽  
Unconstrained Minimization ◽  
Estimation Accuracy ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Risk Minimization ◽  
Training Time ◽  
Minimization Principle ◽  
Empirical Risk ◽  
Benchmark Datasets

Twin support vector machine (TWSVM) exhibits fast training speed with better classification abilities compared with standard SVM. However, it suffers the following drawbacks: (i) the objective functions of TWSVM are comprised of empirical risk and thus may suffer from overfitting and suboptimal solution in some cases. (ii) a convex quadratic programming problems (QPPs) need to be solve, which is relatively complex to implement. To address these problems, we proposed two smoothing approaches for an implicit Lagrangian TWSVM classifiers by formulating a pair of unconstrained minimization problems in dual variables whose solutions will be obtained by solving two systems of linear equations rather than solving two QPPs in TWSVM. Our proposed formulation introduces regularization terms to each objective function with the idea of maximizing the margin. In addition, our proposed formulation becomes well-posed model due to this term, which introduces invertibility in the dual formulation. Moreover, the structural risk minimization principle is implemented in our formulation which embodies the essence of statistical learning theory. The experimental results on several benchmark datasets show better performance of the proposed approach over existing approaches in terms of estimation accuracy with less training time.

A Classification Learning Research based on Discriminative Knowledge-Leverage Transfer

2018 ◽  
Vol 9 (4) ◽  
pp. 52-68 ◽  
Author(s):  
Ding Xiong ◽  
Lu Yan
Keyword(s):  
Transfer Learning ◽  
Data Sets ◽  
Empirical Risk Minimization ◽  
Risk Minimization ◽  
Target Domain ◽  
Source Domain ◽  
The Real ◽  
Empirical Risk ◽  
Real Scene ◽  
Source Data

Current transfer learning models study the source data for future target inferences within a major view, the whole source data should be used to explore the shared knowledge structure. However, human resources are constrained, the source domain data is collected as a whole in the real scene. However, this is not realistic, this data is associated with the target domain. A generalized empirical risk minimization model (GERM) is proposed in this article with discriminative knowledge-leverage (KL). The empirical risk minimization (ERM) principle is extended to the transfer learning setting. The theoretical upper bound of generalized ERM (GERM) is given for the practical discriminative transfer learning. The subset of the source domain data can be automatically selected in the model, and the source domain data is associated with the target domain. It can solve with only some knowledge of the source domain being available, thus it can avoid the negative transfer effect which is caused by the whole source domain dataset in the real scene. Simulation results show that the proposed algorithm is better than the traditional transfer learning algorithm in simulation data sets and real data sets.

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