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Text classification deals with allocating a text document to a predetermined class. Generally, this involves learning about a class from representations of documents belonging to that class. In this paper, we propose a classifier combination that uses a Multinomial Naïve Bayesian (MNB) classifier along with Bayesian Networks (BN) classifier. The results of two classifiers are combined by taking an...
This research paper proposes an intelligent classification technique to recognize normal and abnormal MRI brain image. Medical image like ECG, MRI and CT-scan images are important way to diagnose disease of human being efficiently. The manual analysis of tumor based on visual inspection by radiologist/physician is the conventional method, which may lead to wrong classification when a large number...
High-accuracy localization in harsh environments is a challenging research problem, mainly due to non-line-of-sight (NLOS) propagation, multipath effect, and multiuser interference. Many techniques have been proposed to address this problem; most of them focus on improving the accuracy of ranging estimation, e.g., NLOS identification and mitigation. In this paper, we take ranging one step further...
This paper presents an empirical procedure for predicting robust remaining useful life (RUL) using a naïve Bayesian classifier (NBC) with time as the response. The method is illustrated using public data for predicting Li-ion battery RUL to end-of-life (EOL). A battery life prediction is obtained using the capacity values up to the prediction time. The root mean squared error (RMSE) is used for performance...
Testing web services for robustness is an effective way of disclosing software bugs. However, when executing robustness tests, a very large amount of service responses has to be manually classified to distinguish regular responses from responses that indicate robustness problems. Besides requiring a large amount of time and effort, this complex classification process can easily lead to errors resulting...
This paper presents a study on attributes reduction, comparing five discriminant analysis techniques: FisherFace, CLDA, DLDA, YLDA and KLDA. Attributes reduction has been applied to the problem of leather defect classification using four different classifiers: C4.5, kNN, Naïve Bayes and Support Vector Machines. The results of several experiments on the performance of discriminant analysis...
Spam sender detection based on email subject data is a complex large-scale text mining task. The dataset consists of email subject lines and the corresponding IP address of the email sender. A fast and accurate classifier is desirable in such an application. In this research, a highly scalable SVM modeling method, named Granular SVM with Random granulation (GSVM-RAND), is designed. GSVM-RAND applies...
How to use the incremental training corpus to improve the question classification accuracy rate in the process of question classification based on statistic learning. A question classification method based on the incremental modified Bayes was presented in this paper. The method used the modified Bayes and combined the incremental learning to correct the parameter by the incremental training set stage...
As the rapid development of the Internet, the occurrence of more and more spam mails becomes harmful to users. Content-based spam filtering technologies become the mainstream anti-spam mail methods so far. Support vector machine (SVM), Bayes, windows and KNN are excellent ones of these technologies and they have advantages and disadvantages respectively. The common shortage of content-based methods...
Support vector machine (SVM) is a new learning machine based on the statistical learning theory. A regression algorithm based on least squares support vector machine (LS SVM) within the Bayesian evidence framework is discussed. Also the Gauss kernel parameter selecting method is proposed. Under the evidence framework, the regularization and kernel parameters can be adjusted automatically, which can...
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