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Introduction to the Concept of Structural HMM: Application to Mining Customers' Preferences in Automotive Design
Cambridge UK August 23-August 26
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.133427417th International Conference on Patt ...
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D. Bouchaffra, J. Tan, "Introduction to the Concept of Structural HMM: Application to Mining Customers' Preferences in Automotive Design," Pattern Recognition, International Conference on, vol. 2, pp. 493-496, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 2, 2004.
 
 
BibTex
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@article{ 10.1109/ICPR.2004.1334274,
author = {D. Bouchaffra and J. Tan},
title = {Introduction to the Concept of Structural HMM: Application to Mining Customers' Preferences in Automotive Design},
journal ={Pattern Recognition, International Conference on},
volume = {2},
year = {2004},
issn = {1051-4651},
pages = {493-496},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334274},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Pattern Recognition, International Conference on
TI - Introduction to the Concept of Structural HMM: Application to Mining Customers' Preferences in Automotive Design
SN - 1051-4651
SP493
EP496
A1 - D. Bouchaffra,
A1 - J. Tan,
PY - 2004
KW - null
VL - 2
JA - Pattern Recognition, International Conference on
ER -
 
D. Bouchaffra, Oakland University, Rochester, MI
J. Tan, Oakland University, Rochester, MI
We have introduced in this paper the concept of structural hidden Markov models (SHMM's). This new paradigm adds the syntactical (or structural) component to the traditional HMM's. SHMM's introduce relationships between the visible observations of a sequence. These observations are related because they are viewed as evidences of a same conclusion in a rule of inference. We have applied this novel concept to predict customer's preferences for automotive designs. SHMMhas outperformed both the k-nearest neighbors and the neural network classifiers with an additional 12% increase in accuracy.
Citation:
D. Bouchaffra, J. Tan, "Introduction to the Concept of Structural HMM: Application to Mining Customers' Preferences in Automotive Design," icpr, vol. 2, pp.493-496, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 2, 2004
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