Linear support vector machines via dual cached loops

By Shin Matsushima, S. V. N. Vishwanathan, Alex Smola. 2012

21 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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. Martin Jaggi
2. Mu Li
3. Alex Smola
4. Quoc V. Le
5. Yoshua Bengio
6. Francis R. Bach
7. Dacheng Tao
8. Percy Liang

In Computer Networks and Communications

1. Mu Li
2. D. Andersen
3. Kai Yu
4. Alex Smola
5. Martin Jaggi
6. Celestine Mendler-Dünner
7. Thomas Parnell
8. Tamás Sarlós

Citations in the corpus

• Fastfood: Approximate Kernel Expansions in Loglinear Time (2014, background): “The large scale solvers of Fan et al. (2008) and Matsushima et al. (2012) work in primal space to sidestep these problems, albeit at the cost of limiting themselves to linear kernels, a significantly less powerful function class.”
• Proposal Scaling Distributed Machine Learning with System and Algorithm Co-design (2016, unknown): “In [96] it showed that having both I/O and computational threads working together can further improve the performance.”
• Communication Efficient Distributed Machine Learning with the Parameter Server (2014, methodology): “It is analogous to the active-set selection strategies of SVM optimization [16] and active set selectors [22].”
• Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems (2017, methodology): “In contrast, existing work [2, 7] only showed that the linear convergence rate on SVMs is preserved asymptotically, but not necessarily improved.”