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https://research.cs.wisc.edu/dmi/svm/ssvm/
For more information, see our paper Smooth Support Vector Machines. SVMs are an optimization based approach for solving machine learning problems. For an introduction to SVMs, you may want to look at this tutorial. The software is free for academic use.
https://link.springer.com/article/10.1023%2FA%3A1011215321374
We term such reformulation a smooth support vector machine (SSVM). A fast Newton–Armijo algorithm for solving the SSVM converges globally and quadratically. Numerical results and comparisons are given to demonstrate the effectiveness and speed of the algorithm.Cited by: 754
https://pdfs.semanticscholar.org/fb7e/5403c219b9a49135c21a4580608f8ef1520f.pdf
introduce the smooth support vector machine (SSVM) (17). The smooth sup-port vector machine has important mathematical properties such as strong convexity and infinitely often differentiability. Based on these properties, we can prove that when the smoothing parameter α …
http://math.ntnu.edu.tw/~jschen/Papers/SSVR2017(COAP).pdf
vector machine, we smooth the optimality condition of the strongly convex uncon- strained optimization problem of (3) with another type of smooth functions ψ ε (x,α). Accordingly we define the function H ψ, which also possesses the same properties as
https://dsmilab.github.io/Yuh-Jye-Lee/assets/file/teaching/2017_machine_learning/CASE012407.pdf
Smooth Support Vector Machines for Classification and Regression Lee, Yuh-Jye Research Seminar “Mathematical Statistics” Humboldt University, Berlin, Germany Joint work with Olvi Mangasarian, W.-F. Hsieh, C.-M. Huang, and Sun-Yun Huang January 24, 2007 …
https://github.com/dsmilab/ssvm
Smooth Support Vector Machine Toolbox Introduction. SSVM toolbox is an implementation of Smooth Support Vector Machine in Matlab. SSVM is a reformulation of conventional SVM and can be solved by a fast Newton-Armijo algorithm. Besides, choosing a good parameter setting for a better performance in a learning task is an important issue.
https://www.hindawi.com/journals/mpe/2013/135149/
Support vector machine (SVM) has been applied very successfully in a variety of classification systems. We attempt to solve the primal programming problems of SVM by converting them into smooth unconstrained minimization problems.Cited by: 5
https://www.researchgate.net/publication/226514122_SSVM_A_Smooth_Support_Vector_Machine_for_Classification
We term such reformulation a smooth support vector machine (SSVM). A fast Newton–Armijo algorithm for solving the SSVM converges globally and quadratically. Numerical results and …
https://link.springer.com/article/10.1007%2Fs00376-009-8071-1
Feb 16, 2010 · In this study, a statistical downscaling approach based on the Smooth Support Vector Machine (SSVM) method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the statistical relationship between the larger scale climate predictors and observed precipitation.Cited by: 32
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