Natural language research announcements

[Rajhans Samdani <rsamdan2 AT illinois.edu>]

Hi all, 

Just a gentle reminder of this week's AIIS talk by Pannaga Shivaswamy. The 
talk will be at 2 pm in 3405 SC. Following are the accompanying title and 
abstract. 

Title: Large Relative Margin and Applications 

Abstract: 
Over the last decade or so, machine learning algorithms such as support 
vector   
machines, boosting etc. have become extremely popular. The core idea in these 
and 
other related algorithms is the notion of large margin. Simply put, the idea 
is to 
geometrically separate two classes with a large separation between them; such 
a 
separator is then used to predict the class of an unseen test example. These  
 
methods have been extremely successful in practice and have formed a 
significant 
portion of machine learning literature. There are several theoretical results 
which 
motivate such algorithms. A closer look at such theoretical results reveals 
that the   
generalization ability of these methods are strongly linked to the margin as 
well as 
some measure of the  spread of the data. Yet the algorithms themselves only 
seem to 
be maximizing the margin---completely ignoring the spread information. This 
talk 
focuses on addressing this problem; novel formulations, that not only take 
into 
consideration the margin but also the spread aspect of the data, are 
proposed. In 
particular, relative margin machine, which is  a strict generalization of the 
well known 
support vector machine is proposed. Further, generalization bounds are 
derived for the 
relative margin machines using a novel  method of landmark examples. The idea 
of 
relative margin is fairly general; its potential is demonstrated by proposing 
formulations for structured prediction problems as well as for  a 
transductive setup 
using graph Laplacian. Finally, a boosting algorithm incorporating both the 
margin 
information and the spread information is derived as well. The boosting 
algorithm is 
motivated from  the recent empirical Bernstein bounds.  All the proposed 
variants of 
the relative margin algorithms are easy to implement, efficiently solvable 
and typically 
show significant improvements over their large margin counterparts--on 
real-world 
datasets. (joint work with Dr. Tony Jebara) 

Hoping to see you all. 
Best, 
Rajhans 


Rajhans Samdani,
Graduate Student,
Dept. of Computer Science,
University of Illinois at Urbana-Champaign.