Distance Weighted Discrimination
J. S. Marron (marronemail.unc.edu)
Abstract: High Dimension Low Sample Size statistical analysis is becoming increasingly important in a wide range of applied contexts. In such situations, it is seen that the popular Support Vector Machine suffers from ``data piling'' at the margin, which can diminish generalizability. This leads naturally to the development of Distance Weighted Discrimination, which is based on Second Order Cone Programming, a modern computationally intensive optimization method.
Keywords: two-class discrimination, second-order cone programming
Category 1: Applications -- Science and Engineering (Statistics )
Category 2: Linear, Cone and Semidefinite Programming (Second-Order Cone Programming )
Citation: Technical Report No. 1339, School of Operations Research and Industrial Engineering, Cornell University (July 2002).
Entry Submitted: 07/25/2002
Modify/Update this entry
|Visitors||Authors||More about us||Links|
Search, Browse the Repository
Give us feedback
|Optimization Journals, Sites, Societies|