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Automated detection of tuberculosis in Ziehl Neelsen

We have shown that one class classifiers are able to detect bacilli in sputum smear images with high accuracy the classifiers exploit the colour and shape features of bacilli and novelty detection confers flexibility in dealing with non bacillus pixels and objects


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Naive Bayes is a very simple algorithm to implement and good results have obtained in most cases It can be easily scalable to larger datasets since it takes linear time rather than by expensive iterative approximation as used for many other types of classifiers

Distance based classifier by data transformation for high

We consider classifiers for high dimensional data under the strongly spiked eigenvalue SSE model We first show that high dimensional data often have the SSE model We consider a distance based classifier using eigenstructures for the SSE model We apply

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CiteSeerX Naive Bayes Classifiers A Probabilistic

The prediction is performed from mining the patient s historical data or data repository Further from the experimental results it has been found that Naive Bayes Classifiers is providing improved accuracy with low computational effort and very high speed

Products – re Recognition

Fast processing speed due to small installed footprint and the potential to create bespoke classifiers for specific applications makes KADMOS ideal for applications where speed is critical high volume or always on automated processing is required

Ensemble Classifiers Research for Classify High Speed Data

High speed data stream brings the phenomenon that the data rate is higher relative to the ensemble classifiers computational power so the ensemble classifiers can t train all data which reached recently to update themselves An ensemble classifiers is proposed

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