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Face recognition is one of the most challenging research topics in the field of pattern recognition and computer vision. To efficiently deal with this problem, a novel face recognition algorithm is proposed by using marginal manifold learning and SVM classifier. Extensive experiments show that the proposed algorithm performs much better than other well-known face recognition algorithms.
Dimension reduction is an important data preparation step for face recognition. A new nonlinear dimensionality reduction method called kernel neighborhood preserving embedding (KNPE) is proposed in this paper. This new method extends the well-known neighborhood preserving embedding (NPE) from linear domain to a nonlinear domain with the kernel trick that has been used kernel-based learning algorithms...
To efficiently deal with Web document classification problem, a novel document classification algorithm based on shuffled frog leaping (SFL) algorithm is proposed in this paper. The SFL algorithm combines the benefits of the genetic-based memetic algorithms and the social behavior-based particle swarm optimization algorithms. The experimental results indicate that the proposed SFL algorithm yields...
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