Open Access
Support-vector networks
1
AT&T Bell Labs., Holmdel, USA
|
Publication type: Journal Article
Publication date: 1995-09-01
scimago Q1
wos Q2
SJR: 1.147
CiteScore: 8.6
Impact factor: 2.9
ISSN: 08856125, 15730565
Artificial Intelligence
Software
Abstract
Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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Metrics
34k
Total citations:
33720
Citations from 2025:
3094
(9.17%)
Cite this
GOST |
RIS |
BibTex |
MLA
Cite this
RIS
Copy
TY - JOUR
DO - 10.1007/BF00994018
UR - https://doi.org/10.1007/BF00994018
TI - Support-vector networks
T2 - Machine Learning
AU - CORTES, CORINNA
AU - Vapnik, Vladimir
PY - 1995
DA - 1995/09/01
PB - Springer Nature
SP - 273-297
IS - 3
VL - 20
SN - 0885-6125
SN - 1573-0565
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{1995_CORTES,
author = {CORINNA CORTES and Vladimir Vapnik},
title = {Support-vector networks},
journal = {Machine Learning},
year = {1995},
volume = {20},
publisher = {Springer Nature},
month = {sep},
url = {https://doi.org/10.1007/BF00994018},
number = {3},
pages = {273--297},
doi = {10.1007/BF00994018}
}
Cite this
MLA
Copy
CORTES, CORINNA, and Vladimir Vapnik. “Support-vector networks.” Machine Learning, vol. 20, no. 3, Sep. 1995, pp. 273-297. https://doi.org/10.1007/BF00994018.