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This paper describes a progress of the previous study on the generalized constraint neural networks (GCNN). The GCNN model aims to utilize any type of priors in an explicate form so that the model can achieve improved performance and better transparency. A specific type of priors, that is, equality function constraints, is investigated in this work. When(More)
This work is a further study on the Generalized Constraint Neural Network (GCNN) model [1], [2]. Two challenges are encountered in the study, that is, to embed any type of prior information and to select its imposing schemes. The work focuses on the second challenge and studies a new constraint imposing scheme for equality constraints. A new method called(More)
This paper proposes a locality correlation discriminant with neighborhood preserving embedding for face recognition, which considers both the locality correlation and manifold structure of the training data. A new locality correlation preserving within-class scatter matrix is defined, which not only contains the locality preserving information but also(More)
Attributes of an electronic cash system which is brought up in paper<sup>[1]</sup> are analyzed. The system based on limited blind signature is pointed out that it isn't satisfied with atomicity. At the same time this system also doesn't meet the fair anonymity in some degree. These defects are improved by imposing a trustable third trust part(TTP)to(More)
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