Sciweavers

Share
ICPR
2008
IEEE

Embedding HMM's-based models in a Euclidean space: The topological hidden Markov models

9 years 8 months ago
Embedding HMM's-based models in a Euclidean space: The topological hidden Markov models
One of the major limitations of HMM-based models is the inability to cope with topology: When applied to a visible observation (VO) sequence, HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named “topological hidden Markov models” (THMM’s) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. We have applied the concept of THMM’s to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map a protein primary structure to its 3D fold. The results show that the concept of second level THMM’s outperforms the SHMM’s and the SVM classifiers.
Djamel Bouchaffra
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICPR
Authors Djamel Bouchaffra
Comments (0)
books