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Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier...
Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier...
Binary classification is a process of classifying the elements of a data set into two groups on the basis of a classification rule. It is useful and widely applied in many fields: Information Technology, Business, Medical Diagnosis, Finance, and so on. The problems of the previous works do not specify clearly which classifier utilizes to minimize which type of false, False Positive (FP) or False Negative...
Leveraging advances in transcriptome profiling technologies (RNA-seq), biomedical scientists are collecting everincreasing gene expression profiles data with low cost and high throughput. Therefore, automatic knowledge extraction methods are becoming essential to manage them. In this work, we present GELA (Gene Expression Logic Analyzer), a novel pipeline able to perform a knowledge discovery process...
Proper parameter settings of support vector machine (SVM) and feature selection are of great importance to its efficiency and accuracy. In this paper, we propose a parallel adaptive particle swarm optimization algorithm to simultaneously perform the parameter optimization and feature selection for SVM, termed PTVPSO-SVM. It is implemented in an efficient parallel environment using PVM (Parallel Virtual...
We propose a modular solver for training Support Vector Machines(SVMs) in this paper. Based on the decomposition method(DM), which is the state-of-the-art way for training SVMs with nonlinear kernels, the new solver contains several modules, such as data representation, kernel function evaluation, problem construction, working set selection, sub problem solution, cache maintenance, etc.. The working...
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