A comparison of NN-based and SVR-based power prediction for mobile DS/CDMA systems

Suyaroj, Naret; Theera-Umpon, Nipon; Auephanwiriyakul, Sansanee

doi:10.1109/ispacs.2009.4806718

Cited by 2 publications

(7 citation statements)

References 7 publications

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“…The performance of v-MADR is assessed on both artificial and real datasets in comparison with other typical regression algorithms, such as v-SVR, LS-SVR, ε-TSVR, and linear ε-SVR. According to previous research, SVR-based algorithms show better generalization ability for regression problems [8]- [10]. In conclusion, our experimental results demonstrate that the proposed v-MADR can lead to better performance than other algorithms for regression problems.…”

Section: Introductionsupporting

confidence: 69%

“…However, Formula (10) is still intractable because of the high dimensionality of φ (x) and its complicated form. Inspired by [20], [33], we give the following theorem to state the optimal solution w for Formula (10).…”

Section: B Algorithms For V-madrmentioning

confidence: 99%

“…where the equal relationship in the above '' ≥ is valid if and only if z = 0. Thus, setting z = 0 does not affect the rest of the terms and strictly reduces the first term of Formula (10). Based on all above, w in Formula (10) can be represented as the form of Equation (11).…”

Section: B Algorithms For V-madrmentioning

confidence: 99%

“…SVR realizes linear regression mainly by constructing linear decision functions in high dimensional space. Compared with other regression methods, such as least square regression [4], Neural Networks (NN) regression [5], logistic regression [6], and ridge regression [7], SVR has better generalization ability for regression problems [8]- [10]. In recent years, there have been many studies about SVR-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient v-Minimum Absolute Deviation Distribution Regression Machine

et al. 2020

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Support Vector Regression (SVR) and its variants are widely used regression algorithms, and they have demonstrated high generalization ability. This research proposes a new SVR-based regressor : v-minimum absolute deviation distribution regression (v-MADR) machine. Instead of merely minimizing structural risk, as with v-SVR, v-MADR aims to achieve better generalization performance by minimizing both the absolute regression deviation mean and the absolute regression deviation variance, which takes into account the positive and negative values of the regression deviation of sample points. For optimization, we propose a dual coordinate descent (DCD) algorithm for small sample problems, and we also propose an averaged stochastic gradient descent (ASGD) algorithm for large-scale problems. Furthermore, we study the statistical property of v-MADR that leads to a bound on the expectation of error. The experimental results on both artificial and real datasets indicate that our v-MADR has significant improvement in generalization performance with less training time compared to the widely used v-SVR, LS-SVR, ε-TSVR, and linear ε-SVR. Finally, we open source the code of v-MADR at https://github.com/AsunaYY/v-MADR for wider dissemination. INDEX TERMS v-support vector regression, absolute regression deviation mean, absolute regression deviation variance, dual coordinate descent algorithm.

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Section: Introductionsupporting

confidence: 69%

Section: B Algorithms For V-madrmentioning

confidence: 99%

Section: B Algorithms For V-madrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient v-Minimum Absolute Deviation Distribution Regression Machine

et al. 2020

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“…Moreover, the Structural Risk Minimization (SRM) in learning SVM algorithm is more powerful than the Empirical Risk Minimization (ERM) in the MLP. It has been shown in several applications that both SVR and MLP provided better regression performance than the linear regression and polynomial regression, e.g., in flood prediction (TheeraUmpon et al, 2008), electric load forecasting (Pahasa and Theera-Umpon, 2008;Abd, 2009), drug concentration estimation (Sumonphan et al, 2008), power systems (Boonprasert et al, 2003), computer networks (Hasegawa et al, 2001), telecommunications (Suyaroj et al, 2009), finance (Song et al, 2010), environment (Mileva-Boshkoska and Stankovski, 2007).…”

Section: Introductionmentioning

confidence: 99%

Moisture Content Prediction of Dried Longan Aril from Dielectric Constant Using Multilayer Perceptrons and Support Vector Regression

Maher¹

2010

American Journal of Applied Sciences

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Problem statement: Estimation of moisture contents of dried food products from their dielectric constants was an important step in moisture measurement systems. The regression models that provide good prediction performance are desirable. Approach: The Multilayer Perceptrons (MLP) and Support Vector Regression (SVR) were applied in this research to predict the moisture contents of dried longan arils from their dielectric constants. The data set was collected from 1500 samples of dried longan aril with five different moisture contents of 10, 14, 18, 22 and 25% Wet basis (Wb.) Dielectric constant of dried longan aril was measured by using our previously proposed electrical capacitance-based system. The results from the MLP and SVR models were compared to that from the linear regression and polynomial regression models. To take into account the generalization of the models, the four-fold cross validation was applied. Results: For the training sets, the average mean absolute errors over three bulk densities of 1.30, 1.45 and 1.60 g cm −3 were 1.7578, 0.6157, 0.3812, 0.3113, 0.0103 and 0.0044% Wb for the linear regression, second-, third-, fourth-order polynomial regression, MLP and SVR models, respectively. For the validation sets, the average mean absolute errors over the three bulk densities were 1.7616, 0.6192, 0.3844, 0.3146, 0.0126 and 0.0093% Wb for the linear regression, 2nd, 3rd and 4th-order polynomial regression, MLP and SVR models, respectively. Conclusion: The regression models based on MLP and SVR yielded better performances than the models based on linear regression and polynomial regression on both training and validation sets. The models based on MLP and SVR also provided robustness to the variation of bulk density. Not only for dried longan aril, the proposed models can also be adapted and applied to other materials or dried food products.

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A comparison of NN-based and SVR-based power prediction for mobile DS/CDMA systems

Cited by 2 publications

References 7 publications

An Efficient v-Minimum Absolute Deviation Distribution Regression Machine

An Efficient v-Minimum Absolute Deviation Distribution Regression Machine

Moisture Content Prediction of Dried Longan Aril from Dielectric Constant Using Multilayer Perceptrons and Support Vector Regression

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