Applicational aspects of support vector machines

Belousov, Alexander; Verzakov, Sergey; Frese, J. von

doi:10.1002/cem.744

Cited by 174 publications

(104 citation statements)

References 16 publications

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“…After learning the features of the class, the SVM recognizes unknown samples as a member of a specific class. SVMs have been shown to perform especially well in multiple areas of biological analyses, especially functional class prediction from microarray gene expression data and chemometrics (24)(25)(26)(27)(28). We constructed an SVM classifier with a nonlinear algorithm with Matlab (version 6.5) (Mathworks, Natick, MA) using the training set of sensor response data from subjects with lung cancer, subjects with noncancer disease, and healthy control subjects.…”

Section: Svm Analysismentioning

confidence: 99%

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Machado

Laskowski²,

Deffenderfer³

et al. 2005

Am J Respir Crit Care Med

537

429

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Rationale: Electronic noses are successfully used in commercial applications, including detection and analysis of volatile organic compounds in the food industry. Objectives: We hypothesized that the electronic nose could identify and discriminate between lung diseases, especially bronchogenic carcinoma. Methods: In a discovery and training phase, exhaled breath of 14 individuals with bronchogenic carcinoma and 45 healthy control subjects or control subjects without cancer was analyzed. Principal components and canonic discriminant analysis of the sensor data was used to determine whether exhaled gases could discriminate between cancer and noncancer. Discrimination between classes was performed using Mahalanobis distance. Support vector machine analysis was used to create and apply a cancer prediction model prospectively in a separate group of 76 individuals, 14 with and 62 without cancer. Main Results: Principal components and canonic discriminant analysis demonstrated discrimination between samples from patients with lung cancer and those from other groups. In the validation study, the electronic nose had 71.4% sensitivity and 91.9% specificity for detecting lung cancer; positive and negative predictive values were 66.6 and 93.4%, respectively. In this population with a lung cancer prevalence of 18%, positive and negative predictive values were 66.6 and 94.5%, respectively. Conclusion: The exhaled breath of patients with lung cancer has distinct characteristics that can be identified with an electronic nose. The results provide feasibility to the concept of using the electronic nose for managing and detecting lung cancer.

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Section: Svm Analysismentioning

confidence: 99%

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Machado

Laskowski²,

Deffenderfer³

et al. 2005

Am J Respir Crit Care Med

537

429

View full text Add to dashboard Cite

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“…SVM have already been used in various fields such as diagnosis ovarian tumor malignancy prediction [10], image classification [11] [12] and spam categorization [13]. Only recently has SVM technology been applied to chemometric issues as a non-linear discrimination [14] [15] and quantitative predictions [16]. An alternate formulation of SVM strategy for regression problems is the Least-Square Support-Vector Machine (LS-SVM) [17].…”

Section: Introductionmentioning

confidence: 99%

Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes

Chauchard¹,

Cogdill²,

Roussel³

et al. 2004

Chemometrics and Intelligent Laboratory Systems

352

250

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Nowadays, near infrared (NIR)technology is being transferred from the laboratory to the industrial world for on-line and portable applications. As a result, new issues are arising, such as the need for increased robustness, or the ability to compensate for non-linearities in the calibration or instrument. Semi-parametric modeling has been suggested as a means for adapting to these complications. In this article, Least-Squared Support Vector Machine (LS-SVM) regression, a semi-parametric modeling technique, is used to predict the acidity of three different grape varieties using NIR spectra. The performance and robustness of LS-SVM regression are compared to Partial Least Square Regression (PLSR) and Multivariate Linear Regression (MLR). LS-SVM regression produces more accurate prediction. However SNV pretreatment is required to improve the model robustness.NIR Spectroscopy Robust calibration LS-SVM PLSR MLR Grapes tartaric and malic acidity.

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“…93,94 Support vector machines derive the class decision from the support vectors containing compressed characteristic information. The margin samples lying at the border between two classes are chosen as training prototypes to get the decision boundary as far away as possible from the prototypes to avoid possible misclassification.…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…95 For a simple classification of two circular Gaussian classes, linear discriminant analysis (LDA) learns more efficiently because it employs less parameters than quadratic discriminant analysis (QDA). LDA also shows better performance in most cases.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Chemometrics and its applications to x‐ray spectrometry

Luo

2006

X-Ray Spectrometry

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Recent developments in curve fitting, multivariate calibration, and pattern recognition in chemometrics, and their application to x-ray spectrometry, are reviewed. Relatively innovated algorithms, namely genetic algorithms, neural networks and support vector machines, are discussed. Together with the three algorithms, the performances of different algorithms are compared briefly, which mainly includes principal component analysis, partial least-squares regression, factor analysis, cluster analysis, nearest neighbor methods, linear discriminant analysis, linear learning machine, and soft independent modeling of class analogy. In general, the chemometrics methods are superior to the conventional methods, such as Fourier transform and Marquardt-Levenberg algorithms, to a certain extent.

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Applicational aspects of support vector machines

Cited by 174 publications

References 16 publications

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes

Chemometrics and its applications to x‐ray spectrometry

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