Friday, 10:30 - 10:55 h, Room: H 2013


Yufeng Liu
Optimization issues on some margin-based classifiers


Margin-based classifiers have been popular in both machine learning and statistics for classification problems. Such techniques have a wide range of applications, from computer science to engineering to bioinformatics. Among various margin-based classifiers, the Support Vector Machine is a well known example. Despite successes, many margin-based classifiers with unbounded loss functions can be sensitive to outliers. To achieve robustness, nonconvex loss functions can be used instead. However, the corresponding optimization problem involves non convex minimization and can be very challenging to implement. In this talk, I will present some connection of such a nonconvex optimization problem with integer programming and illustrate how to solve the problem via mixed-integer programming. Some alternative more efficient approximation algorithms will be discussed as well.


Talk 1 of the invited session Fri.1.H 2013
"Integer programming in data mining" [...]
Cluster 11
"Integer & mixed-integer programming" [...]


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