Analyzing big data with the hybrid interval regression methods

2Citations
Citations of this article
18Readers
Mendeley users who have this article in their library.

Abstract

Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM) to analyze big data. Recently, the smooth support vector machine (SSVM) was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes.

Cite

CITATION STYLE

APA

Huang, C. H., Yang, K. C., & Kao, H. Y. (2014). Analyzing big data with the hybrid interval regression methods. Scientific World Journal, 2014. https://doi.org/10.1155/2014/243921

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free