Abstract
This paper develops a vector space model of a class of probabilistic finite state automata (PFSA) that are constructed from finite-length symbol sequences. The vector space is constructed over the real field, where the algebraic operations of vector addition and the associated scalar multiplication operations are defined on a probability measure space, and implications of these algebraic operations are interpreted. The zero element of this vector space is semantically equivalent to a PFSA, referred to as symbolic white noise. A norm is introduced on the vector space of PFSA, which provides a measure of the information content. An application example is presented in the framework of pattern recognition for identification of robot motion in a laboratory environment.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1127-1141 |
| Number of pages | 15 |
| Journal | Journal of Computer and System Sciences |
| Volume | 78 |
| Issue number | 4 |
| DOIs | |
| State | Published - Jul 1 2012 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Networks and Communications
- Computational Theory and Mathematics
- Applied Mathematics
Cite this
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Vector space formulation of probabilistic finite state automata. / Wen, Yicheng; Ray, Asok.
In: Journal of Computer and System Sciences, Vol. 78, No. 4, 01.07.2012, p. 1127-1141.Research output: Contribution to journal › Article
TY - JOUR
T1 - Vector space formulation of probabilistic finite state automata
AU - Wen, Yicheng
AU - Ray, Asok
PY - 2012/7/1
Y1 - 2012/7/1
N2 - This paper develops a vector space model of a class of probabilistic finite state automata (PFSA) that are constructed from finite-length symbol sequences. The vector space is constructed over the real field, where the algebraic operations of vector addition and the associated scalar multiplication operations are defined on a probability measure space, and implications of these algebraic operations are interpreted. The zero element of this vector space is semantically equivalent to a PFSA, referred to as symbolic white noise. A norm is introduced on the vector space of PFSA, which provides a measure of the information content. An application example is presented in the framework of pattern recognition for identification of robot motion in a laboratory environment.
AB - This paper develops a vector space model of a class of probabilistic finite state automata (PFSA) that are constructed from finite-length symbol sequences. The vector space is constructed over the real field, where the algebraic operations of vector addition and the associated scalar multiplication operations are defined on a probability measure space, and implications of these algebraic operations are interpreted. The zero element of this vector space is semantically equivalent to a PFSA, referred to as symbolic white noise. A norm is introduced on the vector space of PFSA, which provides a measure of the information content. An application example is presented in the framework of pattern recognition for identification of robot motion in a laboratory environment.
UR - http://www.scopus.com/inward/record.url?scp=84858437533&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84858437533&partnerID=8YFLogxK
U2 - 10.1016/j.jcss.2012.02.001
DO - 10.1016/j.jcss.2012.02.001
M3 - Article
AN - SCOPUS:84858437533
VL - 78
SP - 1127
EP - 1141
JO - Journal of Computer and System Sciences
JF - Journal of Computer and System Sciences
SN - 0022-0000
IS - 4
ER -