288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

November 2017, Vol. 29, No. 11, Pages 3078-3093
(doi: 10.1162/neco_a_01013)
© 2017 Massachusetts Institute of Technology
Margin Error Bounds for Support Vector Machines on Reproducing Kernel Banach Spaces
Article PDF (231.98 KB)

Support vector machines, which maximize the margin from patterns to the separation hyperplane subject to correct classification, have received remarkable success in machine learning. Margin error bounds based on Hilbert spaces have been introduced in the literature to justify the strategy of maximizing the margin in SVM. Recently, there has been much interest in developing Banach space methods for machine learning. Large margin classification in Banach spaces is a focus of such attempts. In this letter we establish a margin error bound for the SVM on reproducing kernel Banach spaces, thus supplying statistical justification for large-margin classification in Banach spaces.