Support Vector Regression Modeling for LTCC Interconnection Based on Prior Knowledge

2012 ◽  
Vol 2012 (CICMT) ◽  
pp. 000621-000626
Author(s):  
Lei Xia ◽  
Ruimin Xu ◽  
Bo Yan
Keyword(s):  
Low Temperature ◽  
Support Vector Regression ◽  
Prior Knowledge ◽  
Predictive Ability ◽  
Regression Modeling ◽  
Experimental Results ◽  
Support Vector ◽  
Knowledge Based ◽  
Vertical Interconnection ◽  
Rf Performance

This paper presents a prior knowledge based support vector regression modeling method to characterize the RF performance of the low temperature co-fired ceramic (LTCC) structure. A coarse surrogate is formed by multidimensional Cauchy approximation as the prior knowledge to improve the accuracy of modeling. 3D LTCC based vertical interconnection model is developed as an example using the proposed method. Experimental results show that the developed SVR model perform a better predictive ability.

scholarly journals Prior-knowledge based Green's kernel for support vector regression

2021 ◽  
Author(s):  
Tahir Farooq
Keyword(s):  
Support Vector Regression ◽  
Prior Knowledge ◽  
Kernel Function ◽  
Real World ◽  
Matched Filter ◽  
Kernel Functions ◽  
Support Vector ◽  
Mathematical Framework ◽  
Knowledge Based ◽  
Model Complex

This thesis presents a novel prior knowledge based Green's kernel for support vector regression (SVR) and provides an empirical investigation of SVM's (support vector machines) ability to model complex real world problems using a real dataset. After reviewing the theoretical background such as theory SVM, the correspondence between kernels functions used in SVM and regularization operators used in regularization networks as well as the use of Green's function of their corresponding regularization operators to construct kernel functions for SVM, a mathematical framework is presented to obtain the domain knowledge about the magnitude of the Fourier transform of the function to be predicted and design a prior knowledge based Green's kernel that exhibits optimal regularization properties by using the concept of matched filters. The matched filter behavior of the proposed kernel function provides the optimal regularization and also makes it suitable for signals corrupted with noise that includes many real world systems. Several experiments, mostly using benchmark datasets ranging from simple regression models to non-linear and high dimensional chaotic time series, have been conducted in order to compare the performance of the proposed technique with the results already published in the literature for other existing support vector kernels over a variety of settings including different noise levels, noise models, loss functions and SVM variations. The proposed kernel function improves the best known results by 18.6% and 24.4% on a benchmark dataset for two different experimental settings.

scholarly journals Knowledge-Based Green's Kernel for Support Vector Regression

2010 ◽  
Vol 2010 ◽  
pp. 1-16 ◽  
Author(s):  
Tahir Farooq ◽  
Aziz Guergachi ◽  
Sridhar Krishnan
Keyword(s):  
Support Vector Regression ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Matched Filter ◽  
Good Choice ◽  
Kernel Functions ◽  
Support Vector ◽  
Mathematical Framework ◽  
Knowledge Based ◽  
Benchmark Datasets

This paper presents a novel prior knowledge-based Green's kernel for support vector regression (SVR). After reviewing the correspondence between support vector kernels used in support vector machines (SVMs) and regularization operators used in regularization networks and the use of Green's function of their corresponding regularization operators to construct support vector kernels, a mathematical framework is presented to obtain the domain knowledge about magnitude of the Fourier transform of the function to be predicted and design a prior knowledge-based Green's kernel that exhibits optimal regularization properties by using the concept of matched filters. The matched filter behavior of the proposed kernel function makes it suitable for signals corrupted with noise that includes many real world systems. We conduct several experiments mostly using benchmark datasets to compare the performance of our proposed technique with the results already published in literature for other existing support vector kernel over a variety of settings including different noise levels, noise models, loss functions, and SVM variations. Experimental results indicate that knowledge-based Green's kernel could be seen as a good choice among the other candidate kernel functions.

scholarly journals Prior-knowledge based Green's kernel for support vector regression

2021 ◽  
Author(s):  
Tahir Farooq
Keyword(s):  
Support Vector Regression ◽  
Prior Knowledge ◽  
Kernel Function ◽  
Real World ◽  
Matched Filter ◽  
Kernel Functions ◽  
Support Vector ◽  
Mathematical Framework ◽  
Knowledge Based ◽  
Model Complex

This thesis presents a novel prior knowledge based Green's kernel for support vector regression (SVR) and provides an empirical investigation of SVM's (support vector machines) ability to model complex real world problems using a real dataset. After reviewing the theoretical background such as theory SVM, the correspondence between kernels functions used in SVM and regularization operators used in regularization networks as well as the use of Green's function of their corresponding regularization operators to construct kernel functions for SVM, a mathematical framework is presented to obtain the domain knowledge about the magnitude of the Fourier transform of the function to be predicted and design a prior knowledge based Green's kernel that exhibits optimal regularization properties by using the concept of matched filters. The matched filter behavior of the proposed kernel function provides the optimal regularization and also makes it suitable for signals corrupted with noise that includes many real world systems. Several experiments, mostly using benchmark datasets ranging from simple regression models to non-linear and high dimensional chaotic time series, have been conducted in order to compare the performance of the proposed technique with the results already published in the literature for other existing support vector kernels over a variety of settings including different noise levels, noise models, loss functions and SVM variations. The proposed kernel function improves the best known results by 18.6% and 24.4% on a benchmark dataset for two different experimental settings.

scholarly journals Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm

2016 ◽  
Vol 10 (7) ◽  
pp. 29 ◽  
Author(s):  
Jaber Soltani ◽  
Moosa Kalanaki ◽  
Mohammad Soltani
Keyword(s):  
Support Vector Regression ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Failure Rates ◽  
Input Output ◽  
Generalization Capability ◽  
Comparative Tests

This paper proposes a Support Vector Regression (SVR) based on Fuzzified Input-output Variables which has good comprehensibility as well as satisfactory generalization capability. SVM provides a mechanism to predict data from training ones. Then, results from proposed Fuzzified SVR-PSO (FSVR-PSO) model are compared with other methods; comparative tests are performed using pipe failures data. The analysis and the experimental results show this method has high comprehensibility as well as satisfactory generalization capability.

Robust and SparseLP-Norm Support Vector Regression

2017 ◽  
Vol 21 (6) ◽  
pp. 989-997
Author(s):  
Ya-Fen Ye ◽  
Chao Ying ◽  
Yuan-Hai Shao ◽  
Chun-Na Li ◽  
Yu-Juan Chen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Theoretical Analysis ◽  
Support Vector Regression ◽  
Experimental Results ◽  
Support Vector ◽  
The Absolute

A robust and sparseLp-norm support vector regression (Lp-RSVR) is proposed in this paper. The implementation of feature selection in ourLp-RSVR not only preserves the performance of regression but also improves its robustness. The main characteristics ofLp-RSVR are as follows: (i) By using the absolute constraint,Lp-RSVR performs robustly against outliers. (ii)Lp-RSVR ensures that useful features are selected based on theoretical analysis. (iii) Based on the feature-selection results, nonlinearLp-RSVR can be used when data is structurally nonlinear. Experimental results demonstrate the superiorities of the proposedLp-RSVR in both feature selection and regression performance as well as its robustness.

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