Least Squares Support Vector Machines

Suykens, Johan A. K.; Gestel, Tony Van; Brabanter, Jos De; Moor, Bart De; Vandewalle, Joos

doi:10.1142/9789812776655

Cited by 1,497 publications

(1,406 citation statements)

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“…The most distinguishing part of that approach is the utility of a powerful machine learning method, Least Square Support Vector Machine (LS-SVM). 38 This novel machine learning method sets that paper apart from existing papers.…”

Section: Introductionmentioning

confidence: 99%

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Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model

Wang

Celler

et al. 2007

Ann Biomed Eng

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Abstract-This paper aims to establish a block-structured model to predict oxygen uptake in humans during moderate treadmill exercises. To model the steady state relationship between oxygen uptake (oxygen consumption) and walking speed, six healthy male subjects walked on a motor driven treadmill with constant speed from 2 to 7 km/h. The averaged oxygen uptake at steady state (VO 2 ) was measured by a mixing chamber based gas analysis and ventilation measurement system (AEI Moxus Metabolic Cart). Based on these reliable date, a nonlinear steady state relationship was successfully established using Support Vector Regression methods. In order to capture the dynamics of oxygen uptake, the treadmill velocity was modulated using a Pseudo Random Binary Signal (PRBS) input. Breath by breath analysis of all subjects was performed. An ARX model was developed to accurately reproduce the measured oxygen uptake dynamics within the aerobic range. Finally, a Hammerstein model was developed, which may be useful for implementing a control system for the regulation of oxygen uptake during treadmill exercises.

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Section: Introductionmentioning

confidence: 99%

“…16,38,39 Both LS-SVM and e-insensitivity SVM have the merits of SVM approaches. However, the loss function used by e-insensitivity SVM, only penalizes errors greater than a threshold e. This leads to a sparse representation of the decision rule giving significant algorithmic and representation advantages.…”

Section: Introductionmentioning

confidence: 99%

Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model

Wang

Celler

et al. 2007

Ann Biomed Eng

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“…The performance of SVM models depends on penalty term (C), part of the regularization term in quadratic optimization and kernel function, applied to the training data to improve discriminability in feature space. The RBF kernel for one-against-one classification used in this paper for a pair of support vectors i and j is defined by [20,25]: Table 1 PDF based pairwise dissimilarity metrics used in the SVM models…”

Section: Inductive Learningmentioning

confidence: 99%

Active Relearning for Robust Supervised Training of Emphysema Patterns

et al. 2014

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Radiologists are adept at recognizing the character and extent of lung parenchymal abnormalities in computed tomography (CT) scans. However, the inconsistent differential diagnosis due to subjective aggregation necessitates the exploration of automated classification based on supervised or unsupervised learning. The robustness of supervised learning depends on the training samples. Towards optimizing emphysema classification, we introduce a physician-in-the-loop feedback approach to minimize ambiguity in the selected training samples. An experienced thoracic radiologist selected 412 regions of interest (ROIs) across 15 datasets to represent 124, 129, 139 and 20 training samples of mild, moderate, severe emphysema and normal appearance, respectively. Using multi-view (multiple metrics to capture complementary features) inductive learning, an ensemble of seven unoptimized support vector models (SVM) each based on a specific metric was constructed in less than 6 s. The training samples were classified using seven SVM models and consensus labels were created using majority voting. In the active relearning phase, the ensemble-expert label conflicts were resolved by the expert. The efficacy and generality of active relearning feedback was assessed in the optimized parameter space of six general purpose classifiers across the seven dissimilarity metrics. The proposed just-in-time active relearning feedback with un-optimized SVMs yielded 15 % increase in classification accuracy and 25 % reduction in the number of support vectors. The average improvement in accuracy of six classifiers in their optimized parameter space was 21 %. The proposed cooperative feedback method enhances the quality of training samples used to construct automated classification of emphysematous CT scans. Such an approach could lead to substantial improvement in quantification of emphysema.

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“…Strictly speaking, statistical optimality such as in the filter procedure cannot be guaranteed anymore. We compare 3 induction algorithms to search for the best subset of features, in terms of discriminating normal controls from stroke patients: knearest neighbor, least squares support vector machines (LSSVM, Suykens et al 2002) and a Bayesian classifier with kernel density estimation (KDE), for KDE see (Devroye et al 2001). We use Gaussian kernels and use the maximum likelihood cross-validation method for kernel bandwidth estimation in KDE.…”

Section: Feature Subset Selectionmentioning

confidence: 99%

Posterior probability profiles for the automated assessment of the recovery of patients with stroke from activity of daily living tasks

Dijck

Vaerenbergh

Hulle

2009

Artificial Intelligence in Medicine

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Assessing recovery from stroke has been so far a time consuming procedure in which highly trained clinicians are required. This paper proposes a mechatronic platform which measures low forces and torques exerted by subjects. Class posterior probabilities are used as a quantitative and statistically sound tool to assess motor recovery from these force and torque measurements. The performance of the patients is expressed in terms of the posterior probability to belong to the class of normal subjects. The mechatronic platform together with the class posterior probabilities enables to automate motor recovery assessment without the need for highly trained clinicians. It is shown that the class posterior probability profiles are highly correlated, r 0.8, with the well-established Fugl-Meyer scale assessment in motor recovery. These results have been obtained through careful feature subset selection procedures in order to prune the large feature set being generated. The overall approach is general and can be applied to many other health monitoring systems where different categories (diseased vs. healthy) can be identified.

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Least Squares Support Vector Machines

Cited by 1,497 publications

References 0 publications

Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model

Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model

Active Relearning for Robust Supervised Training of Emphysema Patterns

Posterior probability profiles for the automated assessment of the recovery of patients with stroke from activity of daily living tasks

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