Applications of Support Vector Machines for Pattern Recognition: A Survey

By Hyeran Byun, Seong-Whan Lee. International Workshop on Support Vector Machines, 2002

395 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/307678e15ac03393665052974e83e72df6cb24c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Joaquim R. Martins
2. Ji-chao Li
3. John D. Lee
4. Xiaosong Du
5. Gaetan K. W. Kenway

In Computer Vision and Pattern Recognition

1. Michelle L. Reyes
2. Joaquim R. Martins
3. Ji-chao Li
4. John D. Lee
5. Xiaosong Du
6. Yulan Liang

Citations in the corpus

• Machine Learning in Aerodynamic Shape Optimization (2022, background): “One of the most appealing aspects of SVM is the versatility which fits SVM well into a wide range of real-world applications [132, 133].”
• Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits (2018, methodology): “Following the structural risk minimisation (SrM) principle, SVM can effectively overcome over-fitting and under-fitting problems and provides a greater generalisation ability (Byun & Lee 2002; Guo et al. 2006; Qian et al. 2010).”
• Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines (2007, background): “The fixation position and duration may relate to attention orientation and the amount of information perceived from the fixated location, respectively [19].”