Hongliang Fei
homepage: | http://people.eecs.ku.edu/~hfei/ |
search externally: | Google Scholar, Springer, CiteSeer, Microsoft Academic Search, Scirus , DBlife |
Description
Hongliang's research focues on feature selection techniques for data sets with low sample size but high dimensionality in which the features have a structured relationship, such as a chain, a tree and a general graph. The data sets are diverse including both vectorial data such as Miroarray and semi-structured data such as graphs. The goal of his research is to build more accurate and interpretable regression or classification models. He is also interested in sparse learning, multi-task learning with structured input and output.
Lecture:
lecture Boosting with Structure Information in the Functional Space: an Application to Graph Classification as author at Research Sessions, 2896 views |