Incremental Support Vector Learning for Ordinal Regression

@article{Gu2015IncrementalSV,
  title={Incremental Support Vector Learning for Ordinal Regression},
  author={Bin Gu and Victor S. Sheng and KengYeow Tay and Walter Romano and Shuo Li},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2015},
  volume={26},
  pages={1403-1416}
}
Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR. Recently, an interesting accurate on-line algorithm was proposed for training ν-support vector classification (ν-SVC), which can handle a quadratic formulation with a pair of equality constraints. In this paper, we first present a modified SVOR… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 336 CITATIONS, ESTIMATED 87% COVERAGE

Majority Voting and Pairing with Multiple Noisy Labeling

  • IEEE Transactions on Knowledge and Data Engineering
  • 2019
VIEW 7 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Battery-Free and Energy-Effective RFID Sensor Tag for Health Monitoring in Smart Grid

  • 2017 Asia Modelling Symposium (AMS)
  • 2017
VIEW 6 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Multi-Task Rank Learning for Image Quality Assessment

Long Xu, Jia Li, +4 authors Yihua Yan
  • IEEE Transactions on Circuits and Systems for Video Technology
  • 2017
VIEW 8 EXCERPTS
HIGHLY INFLUENCED

An Incremental Dual nu-Support Vector Regression Algorithm

VIEW 9 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Multimodal deep learning for solar radio burst classification

  • Pattern Recognition
  • 2017
VIEW 8 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2015
2019

CITATION STATISTICS

  • 14 Highly Influenced Citations

  • Averaged 77 Citations per year from 2017 through 2019

References

Publications referenced by this paper.
SHOWING 1-10 OF 30 REFERENCES

Feasibility and Finite Convergence Analysis for Accurate On-Line $\nu$-Support Vector Machine

  • IEEE Transactions on Neural Networks and Learning Systems
  • 2013
VIEW 13 EXCERPTS
HIGHLY INFLUENTIAL

Accurate on-line v-support vector learning

  • Neural Networks
  • 2012
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

Support Vector Ordinal Regression

  • Neural Computation
  • 2007
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Robust Support Vector Regression for Uncertain Input and Output Data

  • IEEE Transactions on Neural Networks and Learning Systems
  • 2012
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Similar Papers

Loading similar papers…