A tutorial on?-support vector machines

By Pai-Hsuen Chen, Chih-Jen Lin, Bernhard Sch lkopf. 2005

605 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6eaecfcb51a3edd5fb7b25c3d7a20f555b9e7b76

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Towards Knowledgeable Supervised Lifelong Learning Systems (2020, methodology): “For refining f (s) 1 using HRSVM, the parameter ν is set to the maximal feasible value (Chen et al., 2005), and Γ = 0.”
An Exponential Lower Bound on the Complexity of Regularization Paths (2009, methodology): “The primal ν-SVM problem [7] is the following pQP (the related C-SVM and ar X iv :0 90 3.”
PRoNTo: Pattern Recognition for Neuroimaging Toolbox (2013, methodology): “In the current version of PRoNTo, two kernel classification algorithms are embedded in the framework: Support Vector Machines (SVM, Burges 1998, LIBSVM implementation 13 ) 14 and (binary and multiclass) Gaussian Process classification (GPC, Rasmussen and Williams, 2006, GPML toolbox 15 ).”
SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results (2010, methodology): “This method was shown to perform as well as other algorithms and often better [32].”
Support Vector Machines for TCP traffic classification (2009, methodology): “Although SVMs were originally designed for binary classifiers, Schölkopf et al. in [19] proposed an approach to estimate, for a given class, the spatial region that contains most of the class patterns.”
Glaucoma risk index: Automated glaucoma detection from color fundus images (2010, background): “This SVM classifier determines a maximum-margin and soft hyperplane that best separates the considered classes in a kernel transformed feature space (Chen et al. 2005; Schölkopf et al. 2000).”
On Issues of Instance Selection (2002, background): “Support vector machines (SVM) provide a principled way of finding these points through minimizing structural risk (refer to a tutorial in Burges, 1998).”
Incremental learning for ν-Support Vector Regression (2015, methodology): “We implement our proposed INSVR in MATLAB.”
A Survey on Machine-Learning Techniques in Cognitive Radios (2013, background): “The interested reader is referred to [160], [161] for insightful coverage o f SVM’s.”