Computer Science Department
School of Computer Science, Carnegie Mellon University
New Event Detection with nearest Neighbor,
Support Vector Machines, and Kernel Regression
Jian Zhang, Yiming Yang, Jaime Carbonell
April 2004
This document also appears as Language Technologies Institute
Technical Report CMU-LTI-04-180
CMU-CS-04-118.ps
CMU-CS-04-118.pdf
Support Vector Machines have received extensive attention in machine
learning community and have been successfully applied in pattern
recognition and regression problems. Recently, it has also been
proposed to solve novelty detection problems, whose objective
is to detect novel objects from existing instances. New Event
Detection (NED), which can be treated as one special application
of novelty detection, has been a research topic in Topic Detection
and Tracking (TDT) community for several years. However, the
winning technology of NED in the TDT community has remained
to be the nearest neighbor method with suitable
distance metric in the document vector space. In this paper
we investigated Support Vector Machines and kernel regression
(as a smoothed nearest neighbor method) for the NED task, and
compared them to the nearest neighbor method. We conducted a set
of experiments on TDT benchmark collections, and provided analysis
on the failure of SVM for not being able to capture misses.
36 pages
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