Quantum Support Vector Regression for Disability Insurance

We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The modeling of logistic disability inception probabilities is formulated as a support vector regression problem. Using a quantum feature map, the data are mapped to quantum states belonging to a quantum feature space, where the associated kernel is determined by the inner product between the quantum states. This quantum kernel can be efficiently estimated on a quantum computer. We conduct experiments on the IBM Yorktown quantum computer, fitting the model to disability inception data from a Swedish insurance company.

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A Hybrid Model of Maximum Margin Clustering Method and Support Vector Regression for Noninvasive Electrocardiographic Imaging

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/436281 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Mingfeng Jiang ◽

Feng Liu ◽

Yaming Wang ◽

Guofa Shou ◽

Wenqing Huang ◽

...

Keyword(s):

Support Vector Regression ◽

Small Sample ◽

The Body ◽

Support Vector ◽

Regression Problem ◽

Electrocardiographic Imaging ◽

Maximum Margin Clustering ◽

Maximum Margin ◽

Computation Efficiency ◽

Training Samples

Noninvasive electrocardiographic imaging, such as the reconstruction of myocardial transmembrane potentials (TMPs) distribution, can provide more detailed and complicated electrophysiological information than the body surface potentials (BSPs). However, the noninvasive reconstruction of the TMPs from BSPs is a typical inverse problem. In this study, this inverse ECG problem is treated as a regression problem with multi-inputs (BSPs) and multioutputs (TMPs), which will be solved by the Maximum Margin Clustering- (MMC-) Support Vector Regression (SVR) method. First, the MMC approach is adopted to cluster the training samples (a series of time instant BSPs), and the individual SVR model for each cluster is then constructed. For each testing sample, we find its matched cluster and then use the corresponding SVR model to reconstruct the TMPs. Using testing samples, it is found that the reconstructed TMPs results with the MMC-SVR method are more accurate than those of the single SVR method. In addition to the improved accuracy in solving the inverse ECG problem, the MMC-SVR method divides the training samples into clusters of small sample sizes, which can enhance the computation efficiency of training the SVR model.

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PM10 Prediction Model by Support Vector Regression Based on Particle Swarm Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.3693 ◽

2011 ◽

Vol 403-408 ◽

pp. 3693-3698 ◽

Cited By ~ 1

Author(s):

Saowalak Arampongsanuwat ◽

Phayung Meesad

Keyword(s):

Particle Swarm Optimization ◽

Prediction Model ◽

Support Vector Regression ◽

Mean Squared Error ◽

Particle Swarm ◽

Solution Space ◽

Support Vector ◽

Regression Problem ◽

Swarm Optimization ◽

Temperature Forecasting

This paper proposes a prediction model for the PM10 forecasting in Bangkok. Particulate matter (PM10) with aerodynamic diameter up to 10 m (PM10) is targeted because these small particles effects people’s health and it constitutes major conSubscript textcern for the air quality of Bangkok. Support vector regression (SVR) has been successfully employed to solve regression problem of nonlinearity. The determination for hyper-parameters including kernel parameters and the regularization is important to the performance of SVR. Particle swarm optimization (PSO) is a method for finding a solution of stochastic global optimizer based on swarm intelligence. Using the interaction of particles, PSO searches the solution space intelligently and finds out the best one. Thus, the proposed forecasting model based on the global optimization of PSO and local accurate searching of SVR is applied to forecast PM10 in this paper. The results of this research show the practical prediction model of PM10 based on PSO-SVR is established with C = 5009, ε = 0.0011, σ = 0.1072. The mean squared error (MSE) of the prediction model using PSO-SVR is about 8.654610-11. Practical results indicate that the application of the PSO-SVR method to temperature forecasting of PM10 is feasible and effective. The results show that the model is effective and highly accurate in the forecasting of PM10.

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Robustε-Support Vector Regression

Mathematical Problems in Engineering ◽

10.1155/2014/373571 ◽

2014 ◽

Vol 2014 ◽

pp. 1-5 ◽

Cited By ~ 3

Author(s):

Yuan Lv ◽

Zhong Gan

Keyword(s):

Regression Model ◽

Support Vector Regression ◽

Numerical Experiments ◽

Input Data ◽

System Parameter ◽

Robust Regression ◽

Regression Function ◽

Support Vector ◽

Regression Problem ◽

Robust Support Vector Regression

Spheroid disturbance of input data brings great challenges to support vector regression; thus it is essential to study the robust regression model. This paper is dedicated to establish a robust regression model which makes the regression function robust against disturbance of data and system parameter. Firstly, two theorems have been given to show that the robust linearε-support vector regression problem could be settled by solving the dual problems. Secondly, it has been focused on the development of robust support vector regression algorithm which is extended from linear domain to nonlinear domain. Finally, the numerical experiments result demonstrates the effectiveness of the models and algorithms proposed in this paper.

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Study on Parameter Optimization for Support Vector Regression in Solving the Inverse ECG Problem

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/158056 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Mingfeng Jiang ◽

Shanshan Jiang ◽

Lingyan Zhu ◽

Yaming Wang ◽

Wenqing Huang ◽

...

Keyword(s):

Support Vector Regression ◽

Body Surface ◽

Optimization Methods ◽

Pso Algorithm ◽

Parameters Optimization ◽

Support Vector ◽

Regression Problem ◽

Generalization Performance ◽

Surface Potentials ◽

Transmembrane Potentials

The typical inverse ECG problem is to noninvasively reconstruct the transmembrane potentials (TMPs) from body surface potentials (BSPs). In the study, the inverse ECG problem can be treated as a regression problem with multi-inputs (body surface potentials) and multi-outputs (transmembrane potentials), which can be solved by the support vector regression (SVR) method. In order to obtain an effective SVR model with optimal regression accuracy and generalization performance, the hyperparameters of SVR must be set carefully. Three different optimization methods, that is, genetic algorithm (GA), differential evolution (DE) algorithm, and particle swarm optimization (PSO), are proposed to determine optimal hyperparameters of the SVR model. In this paper, we attempt to investigate which one is the most effective way in reconstructing the cardiac TMPs from BSPs, and a full comparison of their performances is also provided. The experimental results show that these three optimization methods are well performed in finding the proper parameters of SVR and can yield good generalization performance in solving the inverse ECG problem. Moreover, compared with DE and GA, PSO algorithm is more efficient in parameters optimization and performs better in solving the inverse ECG problem, leading to a more accurate reconstruction of the TMPs.

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One Approach for Direct Torque Control Switching Voltage Vector Selection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.246-247.867 ◽

2012 ◽

Vol 246-247 ◽

pp. 867-871

Author(s):

De Cheng Wang ◽

Er Hao Liu ◽

Hui Lin

Keyword(s):

Support Vector Regression ◽

Asynchronous Motor ◽

Direct Torque Control ◽

Nonlinear Function ◽

Classification Problem ◽

Torque Control ◽

Support Vector ◽

Switching Voltage ◽

Regression Problem ◽

Voltage Vector

Direct torque control selected switching voltage vector according to torque hysteresis comparator output, flux hysteresis comparator output, and sector. One switching voltage vector selection approach was proposed. It used support vector regression machine to carry out direct torque control switching voltage vector selection. The selection of eight switching voltage vectors was an eight classification problem. This classification problem was changed into regression problem by support vector regression machine. The nonlinear function used for switching voltage vector selection was gained by support vector regression machine training. Asynchronous motor direct torque control simulation result shows feasibility and effectivity of proposed method.

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On the Use of Support Vector Regression Technique for the Analysis of Rod Ejection Accidents

Volume 5: Fusion Engineering; Student Paper Competition; Design Basis and Beyond Design Basis Events; Simple and Combined Cycles ◽

10.1115/icone20-power2012-54266 ◽

2012 ◽

Author(s):

Botao Jiang ◽

Yang Liu ◽

Fuyu Zhao

Keyword(s):

Support Vector Regression ◽

Superior Performance ◽

Training Dataset ◽

Support Vector ◽

Ann Model ◽

Regression Problem ◽

Pressurized Water ◽

Large Power ◽

Comparison Results ◽

Accident Scenarios

The rod ejection accident (REA) is the design-basis reactivity initiated event and an important aspect for a pressurized water reactor (PWR). The consequence of REA is that it introduces a large positive reactivity insertion in a core, which leads to a fast large power excursion and other parameters changing. Thus, it is important to understand the uncertainty in the parameters of reactor core when REA happens. This paper applies support vector regression (SVR) to analyze accident scenarios with control rod ejection. SVR is an approach based on machine learning and soft computing. SVR, by definition, is an application of support vector machine (SVM) to nonlinear regression problem. Furthermore, the objective of this paper is to train SVR model to identify both safe and potentially unsafe power plant conditions based on real time plant data. The data is obtained from computer generated accident scenarios and is divided into two datasets, training datasets and test datasets. The training dataset are used to train the SVR model and the test dataset are used to test the validation of this model. And then the results obtained by SVR model are compared with that of artificial neural network (ANN) model. The comparison results show that SVR model has superior performance over ANN model and agree well with the general understanding. Because the proposed methodology achieve accurate results, it is likely to be suitable for other data processing of nuclear engineering.

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