BISC Seminar Announcement May 6th, 1999, 4-5pm, 310 Soda

Frank Hoffmann (fhoffman@cs.berkeley.edu)
Mon, 10 May 1999 15:21:53 +0200 (MET DST)

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Berkeley Initiative in Soft Computing (BISC)
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B I S C S e m i n a r A n n o u n c e m e n t
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DISCRIMINATIVE POWER OF INPUT FEATURES IN A FUZZY MODEL



Speaker :

Rosaria Silipo
ICSI, International Computer Science Institute
E-mail:rosaria@ICSI.Berkeley.EDU

Date: Thursday, May 6th, 1999
Time: 4-5pm
Location : 310 Soda Hall

Abstract

In many modern data analysis scenarios the first and most urgent
task consists of reducing the redundancy in high dimensional
input spaces.
In real world applications, fuzzy models allow the introduction
of some uncertainty degree into the analysis and supply several
computationally efficient algorithms.
A method is presented that quantifies the discriminative power of
the input features in a fuzzy model. A possibilistic information
measure of the model is defined on the basis of the available
fuzzy rules and the resulting possibilistic information gain,
associated with the use of a given input dimension, characterizes
the input feature's discriminative power.
Due to the low computational expenses derived from the use of a
fuzzy model, the proposed possibilistic information gain generates
a simple and efficient algorithm for the reduction of the input
dimensionality, even for high dimensional cases.
The method is tested on two real-world examples.
In a fuzzy arrhythmia classification process the most informative
electrocardiographic measures are detected and in a stress speech
recognition task the relative contributions of amplitude, pitch,
and duration of the vowel nuclei are comparatively investigated.

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Please direct questions with regard to the contents of the talk
and request for papers to the speaker.
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Frank Hoffmann                               UC Berkeley
Computer Science Division                    Department of EECS
Email: fhoffman@cs.berkeley.edu              phone: 1-510-642-8282
URL: http://http.cs.berkeley.edu/~fhoffman   fax:  1-510-642-5775
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