Special Colloquium
Junhui Wang
Columbia University
On Large Margin Hierarchical Classification
Abstract: Hierarchical classification is critical to knowledge and context management
as well as knowledge exploration, as in gene function classification and
discovery and document categorization. In hierarchical classification, an input
is classified by a structured hierarchy. In a situation as such, the central
issue is how to effectively utilize inter-class relationship to improve the generalization
performance of flat classification ignoring such dependency. In
this talk, a novel large margin method based on constraints characterizing
multi-path hierarchy is presented within the framework of regularization.
In particular, I will discuss three aspects: (1) the idea and methodology
development; (2) computational tools; (3) a statistical learning theory. Numerical
examples will be provided to demonstrate the advantage of our proposed
methodology against other existing competitors. An application to
gene function prediction and discovery will be discussed.
There will be a meet and greet right after the talk in SEO 300. Coffee, tea, & cookies will be provided.
Tuesday January 29, 2008 at 3:00 PM in SEO 636