Fourth Laboratory of Complex System

Beijing, China

Fourth Laboratory of Complex System

Beijing, China
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Zhou J.D.,PLA Air Force Aviation University | Zhou J.D.,Fourth Laboratory of Complex System | Wang X.D.,PLA Air Force Aviation University | Zhou H.J.,Fourth Laboratory of Complex System | And 2 more authors.
Pattern Recognition | Year: 2012

Ternary Error-Correcting Output Codes (ECOC), which can unify most of the state-of-the-art decomposition frameworks such as one-versus-one, one-versus-all, sparse coding, dense coding, etc., is considered more flexible to model multiclass classification problems than Binary ECOC. Meanwhile, there are many corresponding decoding strategies that have been proposed for Ternary ECOC in earlier literatures. Note that there is few working by posterior probabilities, which can be considered as a Bayes decision rule and hence obtain a better performance in usual. Passerini et al. (2004) [16] have recently proposed a decoding strategy based on posterior probabilities. However, according to the analyses of this paper, Passerini et al.s (2004) [16] method suffers some defects and result in bias. To overcome that, we proposed a variation of it by refining the decomposition process of probability to get smoother estimates. Our biasvariance analysis shows that the decrease in error by our variant is due to a decrease in variance. Besides, we extended an efficient method of obtaining posterior probabilities based on the linear rule for decoding process in Binary ECOC to Ternary ECOC. On ten benchmark datasets, we observe that the two decoding strategies based on posterior probabilities in this paper obtain better performance than other ones in earlier references. © 2011 Elsevier Ltd All rights reserved.


Wang S.S.,PLA Air Force Aviation University | Wang S.S.,Fourth Laboratory of Complex System | Che W.F.,Fourth Laboratory of Complex System | Feng J.F.,PLA Air Force Aviation University | Li M.Z.,Fourth Laboratory of Complex System
Advanced Materials Research | Year: 2011

Traditional methods regard coverage area of radar network as the union of every radar's coverage area. Aiming at this issue, the relationship among radar detection range, radar cross section, signal-to-noise ratio, detection probability and false alarm probability is analyzed. Detection probability model for single radar is established. Calculation method of detection probability for radar network is also researched. Coverage area of radar network can be obtained according to the detection probability. Simulation results show coverage area of radar network is not simply the union of every radar's coverage area and it is decided by the detection probability. Research of this paper provides a theoretical base of detecting, tracking and placement for radar network. © 2011 Trans Tech Publications.


Zhou J.-D.,PLA Air Force Aviation University | Zhou J.-D.,Fourth Laboratory of Complex System | Wang X.-D.,NCO Academy | Zhou H.-J.,Fourth Laboratory of Complex System | And 2 more authors.
Optical Engineering | Year: 2012

It is known that error-correcting output codes (ECOC) is a common way to model multiclass classification problems, in which the research of encoding based on data is attracting more and more attention. We propose a method for learning ECOC with the help of a single-layered perception neural network. To achieve this goal, the code elements of ECOC are mapped to the weights of network for the given decoding strategy, and an object function with the constrained weights is used as a cost function of network. After the training, we can obtain a coding matrix including lots of subgroups of class. Experimental results on artificial data and University of California Irvine with logistic linear classifier and support vector machine as the binary learner show that our scheme provides better performance of classification with shorter length of coding matrix than other state-of-the-art encoding strategies. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE).

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