Big Data" is a term that have jumped overnight from its roots. It can be described as an innovative technique and technology to save, distribute, manage, visualize and analyze larger-sized data with extreme velocity and methods to manage unstructured and structure incapable amount of data. Big data has high capacity to predict conclusion, with low cost consumption, increase efficiency and enhance decision-making in various fields like traffic control ,weather prediction , disaster prevention, finance management, fraud control, improve business transaction, control on national security, education improvement, and health care. Analyzing Big Data is a challenging task as it involves large distributed file systems which should be fault tolerant, flexible and scalable. Various technologies can be used to handle the big data. These technologies handle massive amount of data in MB, PB, YB, ZB, KB, and TB.
Classifying this indefinite big data, is computationally intensive as a large amount of data is related with an existential probability of undefined or undetermined values of raw data. Classifying is hindered by a large amount of data from various sources. RVM, a Bayesian formulation of the linear model both for classification and regression, has lately involved a lot of interest in the research community. The paper aims at learning kernelized RVM classifier to evaluate Ebola virus outbreak, using generalization error, intra class separability, missing probability Pi is compared to SVM.RVM relevance impact with other epidemic diseases of Ebola Virus is also compared.
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