Fixed-size Least Squares Support Vector Machines: A Large Scale Application in Electrical Load Forecasting

Espinoza, Marcelo; Suykens, Johan A. K.; Moor, Bart De

doi:10.1007/s10287-005-0003-7

Cited by 86 publications

(36 citation statements)

References 24 publications

(23 reference statements)

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“…To cope with the large number of datapoints, a fixed-size least squares support vector machine (LS-SVM) was adopted. This method selects a (small) fixed number of training datapoints M (M N) representing the underlying distribution of the dataset through maximization of the quadratic Renyi entropy [29]. A Radial Basis Function (RBF) kernel was used during this active selection of the support vectors, and its bandwidth parameter was computed according to the rule of thumb: The factor was tuned experimentally and set equal to 0.1.…”

Section: Sleep Stage Classificationmentioning

confidence: 99%

Complexity Analysis of Neonatal EEG Using Multiscale Entropy: Applications in Brain Maturation and Sleep Stage Classification

Wel

Lavanga

Caicedo

et al. 2017

Entropy

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Automated analysis of the electroencephalographic (EEG) data for the brain monitoring of preterm infants has gained attention in the last decades. In this study, we analyze the complexity of neonatal EEG, quantified using multiscale entropy. The aim of the current work is to investigate how EEG complexity evolves during electrocortical maturation and whether complexity features can be used to classify sleep stages. First , we developed a regression model that estimates the postmenstrual age (PMA) using a combination of complexity features. Then, these features are used to build a sleep stage classifier. The analysis is performed on a database consisting of 97 EEG recordings from 26 prematurely born infants, recorded between 27 and 42 weeks PMA. The results of the regression analysis revealed a significant positive correlation between the EEG complexity and the infant's age. Moreover, the PMA of the neonate could be estimated with a root mean squared error of 1.88 weeks. The sleep stage classifier was able to discriminate quiet sleep from nonquiet sleep with an area under the curve (AUC) of 90%. These results suggest that the complexity of the brain dynamics is a highly useful index for brain maturation quantification and neonatal sleep stage classification.

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Section: Sleep Stage Classificationmentioning

confidence: 99%

Complexity Analysis of Neonatal EEG Using Multiscale Entropy: Applications in Brain Maturation and Sleep Stage Classification

Wel

Lavanga

Caicedo

et al. 2017

Entropy

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“…Least squares support vector machine (LS-SVM) is reformulations from standard SVM (Vapnik, 1995) which lead to solving linear Karush-Kuhn-Tucker (KKT) systems. LS-SVM is closely related to regularization networks and Gaussian processes but additionally emphasizes and exploits primal-dual interpretations (Espinoza et al, 2006).…”

Section: Function Estimation Using Ls-svmmentioning

confidence: 99%

“…as this is done implicitly through the use of positive definite kernel functions K (Espinoza et al, 2006). From the Lagrange function …”

Section: Function Estimation Using Ls-svmmentioning

confidence: 99%

Hybrid Genetic Algorithm-Support Vector Machine Technique for Power Tracing in Deregulated Power Systems

Mustafa¹,

Sulaiman²,

Khalid³

et al. 2012

Real-World Applications of Genetic Algorithms

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“…LSSVM adopts a least squares linear system as a loss function instead of the quadratic program in original SVM which is time consuming in training process [26][27][28][29][30]. The LSSVM shows manifest advantages, such as good nonlinear fitting ability, strong generalization capability, fast computing speed, dealing with small samples, not relying on the distribution characteristics of the samples and so on [31][32][33][34]. The performance of the LSSVM model is largely dependent on the selection of the parameters.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

Sun

Hong

2015

Energies

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Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM) is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM) energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of σ and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1) (GM (1, 1)), back propagation (BP) and LSSVM, in terms of prediction accuracy and forecasting risk.

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Fixed-size Least Squares Support Vector Machines: A Large Scale Application in Electrical Load Forecasting

Abstract: Least squares support vector machines, Nyström approximation, Fixed-size LS-SVM, Kernel based methods, Sparseness, Primal space regression, Load forecasting, Time series,

Cited by 86 publications

References 24 publications

Complexity Analysis of Neonatal EEG Using Multiscale Entropy: Applications in Brain Maturation and Sleep Stage Classification

Complexity Analysis of Neonatal EEG Using Multiscale Entropy: Applications in Brain Maturation and Sleep Stage Classification

Hybrid Genetic Algorithm-Support Vector Machine Technique for Power Tracing in Deregulated Power Systems

Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

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