An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Cristianini, Nello; Shawe‐Taylor, John

doi:10.1017/cbo9780511801389

Cited by 10,420 publications

(6,881 citation statements)

References 0 publications

Supporting

Mentioning

6,717

Contrasting

Unclassified

150

Order By: Relevance

“…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. 10 On the other hand, the ridge regression (e = 0) used by LS-SVM typically causes the loss of sparseness representation.…”

Section: Introductionmentioning

confidence: 99%

Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model

Wang

Celler

et al. 2007

Ann Biomed Eng

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model

Wang

Celler

et al. 2007

Ann Biomed Eng

View full text Add to dashboard Cite

show abstract

“…For the classifier, we have used the well-known support vector machine (SVM) (Cristianini & Shawe-Taylor, 2000). It is a two class classifier which aims at finding the hyperplane that separates the training patterns of two classes by maximizing the distance between the hyperplane and the two classes.…”

Section: Classification Systemmentioning

confidence: 99%

Protein classification using texture descriptors extracted from the protein backbone image

Nanni

Shi

Brahnam

et al. 2010

Journal of Theoretical Biology

View full text Add to dashboard Cite

show abstract

“…The extensive reviews of SVM methods can be found elsewhere (Bennett et al, 2000;Cristianini and Shawe-Taylor, 2000). The SVM method has a number of interesting properties, including an effective avoidance of overfitting, which improves its ability to build models using large numbers of molecular property descriptors with relatively few experimental results in the training set.…”

Section: Support Vector Machine (Svm) Modelingmentioning

confidence: 99%

Investigation of protein binding affinity and preferred orientations in ion exchange systems using a homologous protein library

Chung¹,

Hou²,

Freed³

et al. 2008

Biotech & Bioengineering

View full text Add to dashboard Cite

A library of cold shock protein B (CspB) mutant variants was employed to study protein binding affinity and preferred orientations in cation exchange chromatography. Single site mutations introduced at charged amino acids on the protein surface resulted in a homologous protein set with varying charge density and distribution. The retention times of the mutants varied significantly during linear gradient chromatography. While the expected trends were observed with increasing or decreasing positive charge on the protein surface, the degree of change was a strong function of the location and microenvironment of the mutated amino acid. Quantitative structure-property relationship (QSPR) models were generated using a support vector regression technique that was able to give good predictions of the retention times of the various mutants. Molecular descriptors selected during model generation were used to elucidate the factors affecting protein retention. Electrostatic potential maps were also employed to provide insight into the effects of protein surface topography, charge density and charge distribution on protein binding affinity and possible preferred binding orientations. The use of this protein mutant library in concert with the qualitative and quantitative analyses presented in the article provides an improved understanding of protein behavior in ion exchange systems.

show abstract

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Cited by 10,420 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

Protein classification using texture descriptors extracted from the protein backbone image

Investigation of protein binding affinity and preferred orientations in ion exchange systems using a homologous protein library

Contact Info

Product

Resources

About