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During the past decade, many efforts have been made to use palmprints as a biometric modality. However, most of the existing palmprint recognition systems are based on encoding and matching creases, which are not as reliable as ridges. This affects the use of palmprints in large-scale person identification applications where the biometric modality needs to be distinctive as well as insensitive to...
Spectrum sensing is essential for the success of the cognitive radio networks. In traditional spectrum sensing schemes, Secondary Users (SUs) are responsible for the spectrum sensing which could be very time and resource consuming. It leads to a great deal of inefficiency in spectrum usage and introduces many practical challenges. To tackle these challenges and leverage the spectrum opportunity more...
Hybrid learning can reduce the computational complexity of incremental algorithms for Bayesian network structures significantly. In this paper, a group of hybrid incremental algorithms are proposed. The central idea of these algorithms is to use the polynomial-time constraint-based technique to build a candidate parent set for each domain variable, followed by the hill climbing search procedure to...
Choosing the kernel and error penalty parameters for support vector machine (SVM) is very important for the performance of classifiers. An improved grid-search algorithm is proposed to choose the optimal parameters of SVM. The battlefield multi-target SVM classifier is designed using this algorithm. Also three classifiers including k-nearest neighborhood classifier, improved BP neural network classifier...
Detection and description of Local feature covariant region is a new technology of image contents and image semantic representations, it has become an important foundation of the image recognition, learning and understanding. First, a Laplace of Gaussian corner detection method is proposed based on edge contour, in the meantime, a new local feature descriptor, named covariant support region, is introduced...
Generalized singular value decomposition (GSVD) has been used for linear discriminant analysis (LDA) to solve the small sample size problem in pattern recognition. However, this algorithm may suffer from the over-fitting problem. In this paper, we propose a novel orthogonalization technique for the LDA/GSVD algorithm to address the over-fitting problem. In this technique, an orthogonalization of the...
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