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http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1535&context=eispapers
Efficient structured support vector regression Abstract Support Vector Regression (SVR) has been a long standing problem in machine learning, and gains its popularity on various computer vision tasks. In this paper, we propose a structured support vector regression
https://www.mathworks.com/help/stats/understanding-support-vector-machine-regression.html
Understanding Support Vector Machine Regression Mathematical Formulation of SVM Regression Overview. Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues in 1992.SVM regression is considered a nonparametric technique because it relies on kernel functions.
https://www.researchgate.net/publication/220745339_Efficient_Structured_Support_Vector_Regression
Support vector regression has been considered as one of the most important regression or function approximation methodologies in a variety of fields.
https://pdfs.semanticscholar.org/994d/751218e9e100ab85a364840827048a2ee7a0.pdf
To enhance the interpretability of the support vector machine, we propose structured learning through functional analysis of variance decomposition. For a general treatment of classification problems, we consider the multicategory support vector machine, an extension of the binary support vector machine proposed by Lee et al. (2004). It is
http://www.columbia.edu/~my2550/papers/svssvm.final.pdf
Wu, Zou and Yuan/Structured support vector machines 105 Rocha and Yu [23] presented the Composite Absolute Penalties which can pro-duce a hierarchical model. Choi and Zhu [5] proposed a penalization method for enforcing the strong heredity principle in fitting a regression model. However,
https://medium.com/coinmonks/support-vector-regression-or-svr-8eb3acf6d0ff
Jun 29, 2018 · This post is about SUPPORT VECTOR REGRESSION. Those who are in Machine Learning or Data Science are quite familiar with the term SVM or Support Vector Machine. But SVR is a bit different from SVM…
https://www.quora.com/What-is-the-difference-between-regular-SVM-and-structural-SVM
Mar 13, 2014 · Structural SVM is a generalization of the SVM to allow structured output (e.g., trees). The standard equation computes a dot product between the learned weights and a feature mapping: [math]<w, \psi(x) y>[/math]. The difference in the structural...
https://people.eecs.berkeley.edu/~trevor/CS294PublicFiles/10Segmentation%20and%20Kernels%20Lecture/blaschko-eccv2008-slides.pdf
Learning to Localize Objects with Structured Output Regression Matthew B. Blaschko and Christoph H. Lampert Max Planck Institute for Biological Cybernetics
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