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https://stats.stackexchange.com/questions/146172/bounded-and-unbounded-support-vectors-for-nu-svms
How do we find out which of the support vectors for a nu-svm are its bounded or unbounded support vectors? For c-SVMs the test is easy: a support vector with $\alpha_i = C$ denotes a bounded support vector, while the others are unbounded.
https://www.stat.berkeley.edu/~arturof/Teaching/EE127/Notes/support_vector_machines.pdf
i is a support vector. Note that the support vectors that satisfy 0 i <C are the unbounded or free support vectors. 3.( i= C): Then by (22), y i wTx i+ b = 1 ˘ i, ˘ i 0, and x iis a SV. Note that the SVs with i= C are bounded support vectors; that is, they lie inside the margin. Furthermore, for 0 ˘ i <1, x i is correctly classi ed, but if ...
https://stackoverflow.com/questions/36295365/more-training-set-errors-than-bounded-support-vectors
As long as there is a nonzero margin, training errors should be a subset of the bounded support vectors, since the bounded support vectors are training instances that are on the wrong side of the margin while training set errors are instance on the wrong side of the learned separator, which lies inside the margin.
https://www.csie.ntu.edu.tw/~cjlin/papers/bottou_lin.pdf
Support Vector Machine Solvers Figure 1: The optimal hyperplane separates positive and negative examples with the max-imal margin. The position of the optimal hyperplane is solely determined by the few examples that are closest to the hyperplane (the support vectors.) 2. Support Vector Machines
https://stats.stackexchange.com/questions/126709/svm-number-of-support-vectors
The support vectors for the negative class vary, but is still quite high. Thus, my questions are: 1) For a linear case, is it reasonable to expect that the number of support vectors from the positive set would be significantly less (given that the data is apparently not linearly seperable).
https://sites.google.com/site/daewonlee/research/svctoolbox
Jan 15, 2009 · Strong: Stable clustering results (4 clusters) Weak: Huge computational burdens for the labeling step induced by considering all pairs on the complete graph. No straightforward way to cluster the bounded support vectors (BSVs) which are classified as the outliers (black points).
https://scikit-learn.org/stable/modules/svm.html
We introduce a new parameter \(\nu\) which controls the number of support vectors and training errors. The parameter \(\nu \in (0, 1]\) is an upper bound on the fraction of training errors and a lower bound of the fraction of support vectors.
https://en.wikipedia.org/wiki/Bound_vector
When it becomes necessary to distinguish these special vectors from vectors as defined in pure mathematics, they are sometimes referred to as geometric, spatial, or Euclidean vectors. Being an arrow, a Euclidean vector possesses a definite initial point and terminal point. A vector with fixed initial and terminal point is called a bound vector.
https://en.wikipedia.org/wiki/Support_vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
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