Abstract
We propose a novel optimization-based paradigm for designing enhanced classifiers. The proposed paradigm allows us to incorporate available prior process knowledge into classifier design, thereby improving the performance of the resulting classifiers. In this work, we focus on dynamical systems that can be represented as finite-state multi-dimensional stochastic processes that possess labeled steady-state distributions. Given prior operational knowledge of the process, our goal is to build a classifier that can accurately label future observations obtained from the steady-state, by utilizing both the available prior knowledge and the training data. Simulation results show that the proposed paradigm yields improved classifiers that outperform traditional classifiers that use only training data.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics |
| Pages | 91-94 |
| Number of pages | 4 |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 - San Antonio, TX, United States Duration: Dec 4 2011 → Dec 6 2011 |
Other
| Other | 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 |
|---|---|
| Country | United States |
| City | San Antonio, TX |
| Period | 12/4/11 → 12/6/11 |
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ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology (miscellaneous)
- Computational Theory and Mathematics
- Signal Processing
- Biomedical Engineering
Cite this
Designing enhanced classifiers using prior process knowledge : Regularized maximum-likelihood. / Esfahani, Mohammad Shahrokh; Zollanvari, Amin; Yoon, Byung Jun; Dougherty, Edward R.
Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics. 2011. p. 91-94 6169451.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Designing enhanced classifiers using prior process knowledge
T2 - Regularized maximum-likelihood
AU - Esfahani, Mohammad Shahrokh
AU - Zollanvari, Amin
AU - Yoon, Byung Jun
AU - Dougherty, Edward R.
PY - 2011
Y1 - 2011
N2 - We propose a novel optimization-based paradigm for designing enhanced classifiers. The proposed paradigm allows us to incorporate available prior process knowledge into classifier design, thereby improving the performance of the resulting classifiers. In this work, we focus on dynamical systems that can be represented as finite-state multi-dimensional stochastic processes that possess labeled steady-state distributions. Given prior operational knowledge of the process, our goal is to build a classifier that can accurately label future observations obtained from the steady-state, by utilizing both the available prior knowledge and the training data. Simulation results show that the proposed paradigm yields improved classifiers that outperform traditional classifiers that use only training data.
AB - We propose a novel optimization-based paradigm for designing enhanced classifiers. The proposed paradigm allows us to incorporate available prior process knowledge into classifier design, thereby improving the performance of the resulting classifiers. In this work, we focus on dynamical systems that can be represented as finite-state multi-dimensional stochastic processes that possess labeled steady-state distributions. Given prior operational knowledge of the process, our goal is to build a classifier that can accurately label future observations obtained from the steady-state, by utilizing both the available prior knowledge and the training data. Simulation results show that the proposed paradigm yields improved classifiers that outperform traditional classifiers that use only training data.
UR - http://www.scopus.com/inward/record.url?scp=84863715202&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863715202&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781467304900
SP - 91
EP - 94
BT - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
ER -