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Bayesian linear classifier is the basic scheme to solve model classification basing on statistics. Face with the classification of three different nectar plant, the near infrared spectrum data was acquired. The character of the near infrared spectrums is known as litter sample and higher dimension. In this paper, the method has developed to acquire the feature wavelength based on genetic algorithm...
Taking advantage of the various available trace transforms generated from a single image, the multiple trace feature (MTF) is proposed as a new image representation. In the process of MTF construction, genetic algorithms (GAs) play a key role as an information fusion tool. The systematic evaluations on a combo face data set comprising ORL, Yale, and UMIST databases reveal that MTF presents high discriminative...
Identification of genes expressed in a cell-cycle-specific periodical manner is of importance and has attracted significant interest recently. However, the identification of cell cycle regulated genes by microarray gene analysis is complicated by both synchronization loss and the presence of noise, and remains an interesting challenge. It is known that periodicity and regulation are two vital components...
Face recognition system usually consists of components of feature extraction and pattern classification. However, not all of extracted facial features contribute to the classification phase positively because of the variations of illumination and poses in face images. In this paper, a three-step feature selection algorithm is proposed in which discrete cosine transform (DCT) and genetic algorithms...
This paper proposes a new face recognition approach by using Kernel Principal Component Analysis (KPCA) and hybrid Flexible Neural Tree (FNT) classification model. To improve the quality of the face images, a series of image pre-processing techniques, which include histogram equalization, edge detection and geometrical transformation etc. The KPCA is employed to extract features for reducing the dimension...
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