By Topic

Support vector regression based autoassociative models for time series classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
S. Chandrakala ; Speech and Vision Lab, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India 600036 ; C Chandra Sekhar

There are two paradigms for modeling varying length time series data, namely, modeling the sequence of feature vectors and modeling the sets of vectors. In this paper, we propose a regression based autoassociative model for modeling sets of vectors for time series data. We also propose a hybrid framework where a regression based autoassociative model is used for representing varying length time series data and then a discriminative model is used for classification. The proposed approach applied to speech emotion recognition task gives a better performance than the conventional methods.

Published in:

Communications (NCC), 2010 National Conference on

Date of Conference:

29-31 Jan. 2010