Classification of Incomplete Pattern Vectors Using Modified Discrminant Functions
IEEE Transactions on Computers
A New Weighted Generalized Inverse Algorithm for Pattern Recognition
IEEE Transactions on Computers
An improved generalized inverse algorithm for linear inequalities and its applications
AFIPS '70 (Spring) Proceedings of the May 5-7, 1970, spring joint computer conference
MUSP'09 Proceedings of the 9th WSEAS international conference on Multimedia systems & signal processing
The use of wavelets in speaker feature tracking identification system using neural network
WSEAS Transactions on Signal Processing
Procrustes analysis and moore-penrose inverse based classifiers for face recognition
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
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Abstract In this paper a least-square approach to multiclass pattern classification is undertaken. The generalized inverse computation is used to furnish a quick solution to the problem of fixed training samples. The use of recursive on-line computation is also recommended. Experimental results are presented to illustrate the approach. Both deterministic and statistical interpretations have been given to the approach. The pattern classifier proposed by Chaplin and Levadi [1] and the adaptive pattern classifier proposed by Patterson and Womack [2] are special cases of this approach.