A tutorial on support vector regression

Smola, Alex; Schölkopf, Bernhard

doi:10.1023/b:stco.0000035301.49549.88

Cited by 9,966 publications

(5,529 citation statements)

References 133 publications

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“…For the prediction of the brain age, we established the framework using a support vector regression (SVR) algorithm (Smola & Schölkopf, 2004) because of its desirable characteristics and easy computation for a high‐dimensional feature space. The estimation model in the basis of SVR algorithm has been widely used in different neuroimaging studies (Dosenbach et al., 2011; Erus et al., 2015; Koutsouleris et al., 2014; Lancaster et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

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The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

2018

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IntroductionWe present the Brain‐Age Score (BAS) as a magnetic resonance imaging (MRI)‐based index for Alzheimer's disease (AD). We developed a fully automated framework for estimating the BAS in healthy controls (HCs) and individuals with mild cognitive impairment (MCI) or AD, using MRI scans.MethodsWe trained the proposed framework using 385 HCs from the IXI and OASIS datasets and evaluated 146 HCs, 102 stable‐MCI (sMCI), 112 progressive‐MCI (pMCI), and 147 AD patients from the J‐ADNI dataset. We used a correlation test to determine the association between the BAS and four traditional screening tools of AD: the Mini‐Mental State Examination (MMSE), Clinical Dementia Ratio (CDR), Alzheimer's Disease Assessment Score (ADAS), and Functional Assessment Questionnaire (FAQ). Furthermore, we assessed the association between BAS and anatomical MRI measurements: the normalized gray matter (nGM), normalized white matter (nWM), normalized cerebrospinal fluid (nCSF), mean cortical thickness as well as hippocampus volume.ResultsThe correlation results demonstrated that the BAS is in line with traditional screening tools of AD (i.e., the MMSE, CDR, ADAS, and FAQ scores) as well as anatomical MRI measurements (i.e., nGM, nCSF, mean cortical thickness, and hippocampus volume).DiscussionThe BAS may be useful for diagnosing the brain atrophy level and can be a reliable automated index for clinical applications and neuropsychological screening tools.

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Section: Methodsmentioning

confidence: 99%

“…More information about the theoretical background of the SVR can be found in (Scholkopf et al., 1997; Smola & Schölkopf, 2004; Vapnik, 2013; Welling, 2004). …”

Section: Methodsmentioning

confidence: 99%

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

2018

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“…30,31 The formulation embodies the Structural Risk Minimization (SRM) principle, 28,29 which has been shown to be superior to the traditional Empirical Risk Minimization (ERM) principle, employed by conventional neural networks. SRM minimizes an upper bound on VC dimension ("generalization error"), as opposed to ERM that minimizes the error on the training data.…”

Section: Support Vector Machine 2829mentioning

confidence: 99%

QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine

Xue

Zhang

Liu

et al. 2004

J. Chem. Inf. Comput. Sci.

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The binding affinities to human serum albumin for 94 diverse drugs and drug-like compounds were modeled with the descriptors calculated from the molecular structure alone using a quantitative structure-activity relationship (QSAR) technique. The heuristic method (HM) and support vector machine (SVM) were utilized to construct the linear and nonlinear prediction models, leading to a good correlation coefficient (R 2 ) of 0.86 and 0.94 and root-mean-square errors (rms) of 0.212 and 0.134 albumin drug binding affinity units, respectively. Furthermore, the models were evaluated by a 10 compound external test set, yielding R 2 of 0.71 and 0.89 and rms error of 0.430 and 0.222. The specific information described by the heuristic linear model could give some insights into the factors that are likely to govern the binding affinity of the compounds and be used as an aid to the drug design process; however, the prediction results of the nonlinear SVM model seem to be better than that of the HM.

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“…This study is motivated by a growing popularity of support vector machines (SVM) for regression problems [3,[6][7][8][9][10][11][12][13][14]. Their practical successes can be attributed to solid theoretical foundations based on VC-theory [13,14], since SVM generalization performance does not depend on the dimensionality of the input space.…”

Section: Introductionmentioning

confidence: 99%

Practical selection of SVM parameters and noise estimation for SVM regression

2004

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We investigate practical selection of meta-parameters for SVM regression (that is, ε -insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than resampling approaches commonly used in SVM applications. Good generalization performance of the proposed parameter selection is demonstrated empirically using several low-dimensional and high-dimensional regression problems. Further, we point out the importance of Vapnik's ε -insensitive loss for regression problems with finite samples. To this end, we compare generalization performance of SVM regression (with optimally chosen ε ) with regression using 'least-modulus' loss (ε =0). These comparisons indicate superior generalization performance of SVM regression, for finite sample settings.

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A tutorial on support vector regression

Cited by 9,966 publications

References 133 publications

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine

Practical selection of SVM parameters and noise estimation for SVM regression

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