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https://nlp.stanford.edu/IR-book/html/htmledition/soft-margin-classification-1.html
The dual problem for soft margin classification becomes: Neither the slack variables nor Lagrange multipliers for them appear in the dual problem. All we are left with is the constant bounding the possible size of the Lagrange multipliers for the support vector data points. As before, the with non-zero will be the support vectors. The solution ...
http://fourier.eng.hmc.edu/e161/lectures/svm/node5.html
2-Norm Soft Margin Up: Support Vector Machines (SVM) Previous: Support Vector Machine Soft Margin SVM. When the two classes are not linearly separable (e.g., due to noise), the condition for the optimal hyper-plane can be relaxed by including an extra term:
https://people.eecs.berkeley.edu/~jordan/courses/281B-spring04/lectures/lec6.pdf
Soft Margin SVM Lecturer: Michael I. Jordan Scribe: Anat Caspi ... 1 SVM Non-separable Classi cation We return to our discussion of classi cation, this time addressing the problems of nonseparable datasets, as ... As before, i will be nonzero only for the support vectors, where the set of support vectors …
https://people.eecs.berkeley.edu/~jrs/189/lec/04.pdf
The support vector classifier,sometimescalledasoft margin classifier, support vector classifier soft margin classifier does exactly this. Rather than seeking the largest possible margin so that every observation is not only on the correct side of the hyperplane but also on the correct side of the margin, we instead allow some observations
https://www.researchgate.net/post/Can_anyone_explain_to_me_hard_and_soft_margin_Support_Vector_Machine_SVM
Can anyone explain to me hard and soft margin Support Vector Machine (SVM)? ... (support vectors) to the training/discovery data set. ... On the other hand soft margin SVM was proposed by Vapnik ...
https://www.cs.cmu.edu/~tom/10701_sp11/slides/Kernels_SVM2_04_12_2011-ann.pdf
SVM Soft Margin Decision Surface using Gaussian Kernel Circled points are the support vectors: training examples with non-zero Points plotted in original 2-D space. Contour lines show constant [from Bishop, figure 7.4] SVM Summary • Objective: maximize margin between decision surface and data • Primal and dual formulations
https://stats.stackexchange.com/questions/180701/support-vector-machine-soft-margin
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