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To improve accuracy and adaptability, this paper presents a learning algorithm for color recognition of license plates. For three components of the hue-saturation-value (HSV) color space, different membership functions were defined to calculate their fuzzy degrees. Through the weighted fusion of the three membership degrees, a single map was produced to be the classification function for color recognition,...
Color recognition of license plates is an important step to License Plate Recognition (LPR) system. In order to perform color recognition more effectively, an algorithm based on Naive Bayesian approach is proposed in this paper. To improve the efficiency of color recognition, the multiclass problem is converted into two binary problems based on the reverse color information of plate images. Color...
Automatically classifying text documents is an important field in machine learning. Unsupervised text classification does not need training data but is often criticized to cluster blindly. Supervised text classification needs large quantities of labeled training data to achieve high accuracy. However, in practice, labeled samples are often difficult, expensive or time consuming to obtain. In the meanwhile,...
Name ambiguity is a critical problem in many applications, in particular in the online bibliography systems, such as DBLP and CiteSeer. Previously, several clustering based methods have been proposed although, the problem still presents to be a big challenge for both research and industry communities. In this paper, we present a complementary study to the problem from another point of view. We propose...
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