Support Vector Machines: Theory and Applications

By Theodoros Evgeniou, Massimiliano Pontil. Machine Learning and Its Applications, 2001

905 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ef4ffbb512d361a72f6933ced157988716193aa9

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. A. Douaik
2. Teresa B Ludermir
3. Eva-Henrietta Dulf
4. Silvio B. Guerra
5. Andrei-Alexandru Tulbure
6. Monther Alhamdoosh
7. Wim Cornelis
8. Ricardo Bastos Cavalcante Prudêncio

In Computer Vision and Pattern Recognition

1. Teresa B Ludermir
2. Eva-Henrietta Dulf
3. Silvio B. Guerra
4. Andrei-Alexandru Tulbure
5. Ricardo Bastos Cavalcante Prudêncio
6. Adrian-Alexandru Tulbure
7. Frank Hutter
8. Katharina Eggensperger

Citations in the corpus

• A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2021, methodology): “Finally, the classification part for the regions is handled by a class-specific linear SVM(support-vector-machine) [78].”
• Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors (2008, methodology): “SVM is a state-of-the-art algorithm in the Machine Learning field, successfully applied to different classification and regression problems [10, 11].”
• Enhanced pedotransfer functions with support vector machines to predict water retention of calcareous soil (2016, methodology): “The SVM approach was developed in the reverse order to that of the ANN (Wang, 2005).”