MULTI-CATEGORY SUPPORT VECTOR MACHINES, FEATURE SELECTION AND SOLUTION PATH

By Lifeng Wang, Xiaotong Shen. 2006

36 citations recorded by the source corpus.

Subfields: Statistical Learning Theory

How researchers see this paper

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

In Statistical Learning Theory

1. Simon Lacoste-Julien
2. Anton Osokin
3. Isabella Lukasewitz
4. Puneet Kumar Dokania
5. Jean-Baptiste Alayrac
6. Donglin Zeng
7. Zhenhua Cui
8. A. Rush

Citations in the corpus

• Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs (2016, background): “Wang & Shen (2006) analyzed the regularization path for the L1 version of MSVM. Finally, Jun-Tao & Ying-Min (2010) constructed the regularization path for the multi-class SVM with huberized loss.”
• CHARACTERIZING THE SOLUTION PATH OF MULTICATEGORY SUPPORT VECTOR MACHINES (2006, methodology): “We also note in passing that for the special case of an l1-norm linear MSVM, Wang and Shen (2005) recently studied an algorithm to construct the solution path for simultaneous fitting and feature selection.”
• Estimating Individualized Treatment Rules Using Outcome Weighted Learning (2012, background): “The case of multicategory SVM has been studied recently (Lee, Lin & Wahba 2004; Wang & Shen 2006), and a similar generalization may be possible for finding ITRs involving three or more treatments.”