Output details
11 - Computer Science and Informatics
University of Portsmouth
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Output 0 of 0 in the submission
Article title
Fuzzy Gaussian Mixture Models
Type
D - Journal article
Title of journal
Pattern Recognition
Article number
-
Volume number
45
Issue number
3
First page of article
1146
ISSN of journal
00313203
Year of publication
2012
URL
-
Number of additional authors
1
Additional information
<13>The novel algorithm of Fuzzy Gaussian Mixture Models enables the conventional Gaussian mixture models with non-linear curve manifolds and fast convergence process. The proposed method brings the conventional Gaussian mixture models to a significantly wider spectrum of applications, especially for real-time applications. It attracts increasing research interest and has been applied and validated in several applications including bio-signal recognition, hand gesture modelling and desired trajectory generation.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
6
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
