TY - GEN
T1 - Designing enhanced classifiers using prior process knowledge
T2 - 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
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
U2 - 10.1109/gensips.2011.6169451
DO - 10.1109/gensips.2011.6169451
M3 - Conference contribution
AN - SCOPUS:84863715202
SN - 9781467304900
T3 - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
SP - 91
EP - 94
BT - Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
PB - IEEE Computer Society
Y2 - 4 December 2011 through 6 December 2011
ER -