A Tutorial on Support Vector Machines for Pattern Recognition

By C. Burges. 1998

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In Statistical Learning Theory

Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “Readers who are interested particularly in the typical applications of kernels in machine learning, e.g., support vector machines (SVMs) and Gaussian processes (GPs), are encouraged to read Cucker and Smale (2002), Burges (1998), Rasmussen and Williams (2005) and references therein.”
Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners (2015, methodology): “Under these conditions, the bilevel problem Eq (5) can be reduced to a single-level constrained optimization problem via the Karush-Kuhn-Tucker (KKT) conditions of the lower-level problem (Burges 1998).”
Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, background): “Although SVMs were initially introduced to solve pattern recognition problems in an eﬃcient way ([20]), nowadays they are suitable in several contexts.”
Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “The quantitative embodiment of this assertion is the radius margin bound on the generalization error of the SVM Burges (1998)(Vapnik, 1995). Yet, the statistics used in a large proportion of previous work, Gaonkar and Davatzikos (2013)Mouro-Miranda et al. (2005)Wang et al. (2007)Rasmussen et al. (20”
Kernel methods in machine learning (2007, neutral): “In doing so, we build on various sources, including Burges [25], Cristianini and Shawe-Taylor [37], Herbrich [64] and Vapnik [141] and, in particular, Schölkopf and Smola [118], but we also add a fair amount of more recent material which helps unifying the exposition.”
DeepEdge: A multi-scale bifurcated deep network for top-down contour detection (2014, methodology): “Whereas sparse code gradients use supervised SVM learning [5], gPb relies on spectral graph theory.”
High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and Its Applications to High-Level Vision (2015, methodology): “While SCG use supervised SVM learning [4], the latter two methods rely on a random forest classifier and models boundary detection as a classification task.”
Enhancing one-class support vector machines for unsupervised anomaly detection (2013, methodology): “This includes for example handwritten digit recognition, object recognition, speaker identification, text categorization [6] and also anomaly detection.”
Support vector machines for temporal classification of block design fMRI data (2005, background): “For a more general treatment, please refer to Burges (1998), Cherkassky and Mulier (1998), and Muller et al. (2001).”
ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction (2008, methodology): “Compared with NN, SVM has the advantage of identifying the global optimum despite longer training time [18].”
Eclectic Rule-Extraction from Support Vector Machines (2005, background): “In case of the SVM, linearly separable data should satisfy the constraints in (2) and (3), [12].”
Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection (2014, methodology): “Lastly, the support vector machine (SVM) [31] was implemented to finetune the risk score.”