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In this paper, Maximum Entropy (ME) framework is used to classify text documents. The ME framework has a lot of advantages when compared with other supervised learning algorithms, such as naive Bayes classifier. For example, it makes no inherent conditional independence assumptions between terms. With four labeled data sets, extensive experiments are made to compare the accuracy of ME algorithm with...
In order to classify the traffic objects in multi-traffic scenes, six classes were divided firstly, then eight features base on shape and motion information are extracted. The eight features of traffic objects will be the input of the support vector machine (SVM) classifier which is contrasted with RBF neural network classifier. The object type is classified according to the output of the SVM. Experimental...
A fingerprint classification method using the fingerprint orientation information and Support Vector Machine (SVM) was proposed. Firstly, the reference point of fingerprint image was acquired, and fingerprint pattern area was located. Then the orientation features were extracted by using Gabor filter. Finally multiclass classifiers were constructed based on SVM and a new three stage classification...
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