Least Squares Support Vector Machines

By Johan A. K. Suykens, Tony Van Gestel, Jos De Brabanter, B. De Moor, Joos Vandewalle. 2002

1,828 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b24972552161cd9eda729e748762a73430983e3a

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Nicola L. C. Talbot
2. Gavin C. Cawley
3. Xin Yao
4. Da‐Wen Sun
5. Wenchuan Guo
6. Tarin Paz-Kagan
7. José Carlos Moreira Bermudez
8. E. Ke Tang

In Control and Systems Engineering

1. Kin Keung Lai
2. Z. Lou
3. Tarin Paz-Kagan
4. Xin Yao
5. E. Ke Tang
6. David Martens
7. Vladimir Cherkassky
8. Rainer Goebel

Citations in the corpus

• Nondestructive Measurement of Soluble Solids Content of Kiwifruits Using Near-Infrared Hyperspectral Imaging (2015, positive): “The details of LSSVM algorithm could be found in the literature (Suykens et al. 2002).”
• Support Vector Machines – An Introduction (2005, methodology): “Notably, they are the linear programming SVMs [10, 13, 14, 15, 16, 18, 27], υ-SVMs [25 ]a nd least squares support vector machines [ 29 ].”
• Regularized linear and kernel redundancy analysis (2007, methodology): “Kernel methods have been popularized by engineers who constantly deal with practical problems (e.g., Herbrich, 2002; Suykens et al., 2002).”