Fast training Support Vector Machines using parallel sequential minimal optimization

By Zhi-Qiang Zeng, Hongyang Yu, Huarong Xu, Yan-Qi Xie, Ji Gao. 2008

134 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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. Raúl Monroy
2. Octavio Loyola-González
3. Marc-André Zöller
4. Armando López-Cuevas
5. Javier Israel Mata-Sánchez
6. Jorge Rodríguez
7. Marco F. Huber
8. Li Yang

In Computer Vision and Pattern Recognition

1. Marc-André Zöller
2. Octavio Loyola-González
3. Jorge Rodríguez
4. Raúl Monroy
5. Marco F. Huber

Citations in the corpus

• Survey on Automated Machine Learning (2019, background): “Furthermore, research exists to parallelize sequential ML algorithms, e.g., (Zeng et al., 2008). scikit-learn (Pedregosa et al., 2011) already implements many optimizations to distribute workload on multiple cores on a single machine.”
• Contrast Pattern-Based Classification for Bot Detection on Twitter (2019, methodology): “SVM: Support Vector Machine [69] applies a kernel function to map the input instances into a highdimensional feature space that splits the different classes and separates them as much as possible.”