Analysis of recent advancements in support vector machine

Abstract

SummaryThe current researches are primarily focused on optimizing the available classification methods in support vector machines (SVMs). The basic idea of this article to highlight the fundamentals of the proposed SVM variants and their optimization techniques. In this article, we have collectively identified the major issues in different variations of SVMs. This article has discussed a detailed explanation of the optimization and advancement of SVM and its kernel variants. The study elaborates on certain SVM optimization issues like model selection, novelty detection and so forth. In contrast, it discussed the identification of problems raises after every optimization and advancement of SVM. This is the first novel attempt to explain the optimization raises for SVMs. This article is about the majority of the necessary advancements in the optimization and cost‐effectiveness of computational algorithms of SVM with their detailed computational analysis. The approaches of such study helps to understand the optimization challenges in SVM.