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One of the important stages for optical character recognition system is text components segmentation from non-text components of input images. In this paper a machine learning technique based on a naive bayes classifier is developed for text components segmentation. In training stage, a simple procedure is used to generate a large collection of training data sets for learning the classifier. A collection...
Naïve Bayes classifier is proved to be one of the most effective classifier an be used widely. It applies statistical theory to text classification. This paper researched and implemented a Chinese text classifier using JAVA base on Naïve Bayes Method. First of all, this paper described test classification system, the content includes text information expressing, extracting and the method of Chinese...
“Binning” (or taxonomic classification) of DNA sequence reads is an initial step to analyzing an environmental biological sample. Currently, a homology-based tool, BLAST, is one of the most commonly used tools to label DNA reads, but it is argued that BLAST will quickly lose its classification ability as the genome databases grow. In this paper, we compare the accuracies of a naïve Bayes classifier...
A classification model is obtained after a classifier is trained on training data. Decision region is the region in which data are predicted the same class label by a classifier. Decision boundary is the boundary between regions of different classes. We view classification as dividing the data space into decision regions. The formal definitions of decision region and decision boundary are presented...
A good concept drifting stream classifier should have the following two characteristics: 1) sensitive to the new concept when concept drifts; 2) have stable high accuracy when concept is stable. Most published methods and algorithms may succeed in one aspect while neglecting the other. In this paper, we proposed an adaptive ensemble classifier for concept drifting stream classification which focuses...
The text representation in text classification is usually a sequence of terms. As the number of terms becomes very high, it is greatly time-consuming to perform existed text categorization tasks. In this paper we presented a novel text representation model for text classification which greatly reduced the required resources. This model represents text with several features. Each feature corresponds...
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