scholarly journals Using Hierarchical Likelihood Towards Support Vector Machine: Theory and Its Application

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 194795-194807
Author(s):  
Rezzy Eko Caraka ◽  
Youngjo Lee ◽  
Rung-Ching Chen ◽  
Toni Toharudin
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Hierarchical Likelihood ◽  
Machine Theory

LS-SVM Adaptive Back-Stepping Control for a Class of Uncertain Nonlinear Systems

2012 ◽  
Vol 588-589 ◽  
pp. 1409-1413
Author(s):  
Guo Dong Zhu ◽  
Hui Lin ◽  
Chen Wang
Keyword(s):  
Support Vector Machine ◽  
Nonlinear Systems ◽  
Least Squares ◽  
System Stability ◽  
Stability And Convergence ◽  
Support Vector ◽  
Control Law ◽  
Uncertain Nonlinear Systems ◽  
Machine Theory ◽  
Back Stepping Control

Based on back-stepping control design, adaptive control and least squares support vector machine theory, a new least squares support vector machine adaptive back-stepping control law was designed for strictly block type of feedback nonlinear systems control with uncertainties. Least squares support vector machine theory method to approximate a nonlinear function of uncertain nonlinear systems by analyzing the disadvantage of common back-stepping. New control law of the nonlinear systems is achieved without accurate mathematical model. The method overcomes the impact of the bounded uncertainties on the control system. On this basis, the system stability and convergence are proved by Lyapunov method. Simulation results indicate that the designed control law has strong robustness and adaptability, uncertainties that exist in the strict block feedback nonlinear systems.

Support Vector Machine and its Application Status

2014 ◽  
Vol 1030-1032 ◽  
pp. 1814-1817
Author(s):  
Lan Lan Kang ◽  
Wen Liang Cao
Keyword(s):  
Support Vector Machine ◽  
Small Error ◽  
Support Vector ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Training Samples ◽  
Testing Error ◽  
Machine Theory ◽  
Learning Machine ◽  
Limited Training Samples

Support vector machine is a beginning of the 1990s, based on statistical learning theory proposed new machine learning method, which structural risk minimization principle as the theoretical basis, by appropriately selecting a subset of functions and discriminant function in the subset, so the actual risk of learning machine to a minimum, to ensure that the limited training samples obtained through a small error classifier, an independent test set for testing error remains small. In this paper, support vector machine theory, algorithm, application status, etc. are discussed in detail.

scholarly journals An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation

2021 ◽  
Vol 16 (5) ◽  
pp. 1571-1583
Author(s):  
Jiafu Su ◽  
Xu Chen ◽  
Fengting Zhang ◽  
Na Zhang ◽  
Fei Li
Keyword(s):  
Support Vector Machine ◽  
Product Innovation ◽  
Identification Accuracy ◽  
Support Vector ◽  
User Identification ◽  
Cost Sensitive Learning ◽  
Lead User ◽  
Machine Theory ◽  
Lead Users ◽  
The Cost

For customer collaborative product innovation (CCPI), lead users are powerful enablers of product innovation. Identifying lead users is vital to successfully carrying out CCPI. In this paper, in order to overcome the shortcomings of traditional evaluation methods, a novel intelligent method is proposed to identify lead users efficiently based on the cost-sensitive learning and support vector machine theory. To this end, the characteristics of lead users in CCPI are first analyzed and concluded in-depth. On its basis, considering the sample misidentification cost and identification accuracy rate, an improved cost-sensitive learning support vector machine (ICS-SVM) method for lead user identification in CCPI is further proposed. A real case is provided to illustrate the effectiveness and advantages of the ICS-SVM method on lead user identification in CCPI. The case results show that the ICS-SVM method can effectively identify lead users in CCPI. This work contributes to user innovation literature by proposing a new way of identifying highly valuable lead users and offers a decision support for the efficient user management in CCPI.

Wind Speed Forecasting Based on Least Squares Support Vector Machine and Particle Swarm Optimization

2014 ◽  
Vol 602-605 ◽  
pp. 3251-3255
Author(s):  
Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Least Squares ◽  
Wind Farm ◽  
Particle Swarm ◽  
Support Vector ◽  
Forecasting Model ◽  
Swarm Optimization ◽  
Machine Theory

This paper is based on Least Squares Support Vector Machine theory to build the wind speed forecasting model. Meanwhile, as there is still no effective choice method of Least Squares Support Vector Ma-chine parameter, this paper tried to use Particle Swarm Optimization theory to optimization choice for parameter. And last, use wind farm observed wind speed (sampling interval is 1 minute) of three days to forecast the next minute wind speed through this paper's wind forecasting model, and prediction result is that the MAPE is only 4.63%, the prediction effect is relative ideal, confirm the feasibility of applying the Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine theory to forecast the wind speed, it will provide theoretical support to wind farm layout and wind power forecasting and so on.

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