A New Alpha Seeding Method for Support Vector Machine Training

Feng, Du; Shi, Wenyu; Guo, Huawei; Liangzhou, Chen

doi:10.1007/11539087_87

Cited by 1 publication

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The geometric methods of SVM are based on the calculation of the optimal separating hyperplane that finds the nearest points to the convex hull [114]. In addition to heuristic methods such as [115], [116], alpha seeding was proposed in [115], [116] to estimate the initial value of to start the QP problem. In [117], the decision tree was employed to propose an SVM decision boundary approximation method.…”

Section: Rq1) What Is the Motivation For Svm Compatibility With Big Data Properties?mentioning

confidence: 99%

Support Vector Machines in Big Data Classification: A Systematic Literature Review

Almaspoor

Safaei

Salajegheh

et al. 2021

Preprint

View full text Add to dashboard Cite

Classification is one of the most important and widely used issues in machine learning, the purpose of which is to create a rule for grouping data to sets of pre-existing categories is based on a set of training sets. Employed successfully in many scientific and engineering areas, the Support Vector Machine (SVM) is among the most promising methods of classification in machine learning. With the advent of big data, many of the machine learning methods have been challenged by big data characteristics. The standard SVM has been proposed for batch learning in which all data are available at the same time. The SVM has a high time complexity, i.e., increasing the number of training samples will intensify the need for computational resources and memory. Hence, many attempts have been made at SVM compatibility with online learning conditions and use of large-scale data. This paper focuses on the analysis, identification, and classification of existing methods for SVM compatibility with online conditions and large-scale data. These methods might be employed to classify big data and propose research areas for future studies. Considering its advantages, the SVM can be among the first options for compatibility with big data and classification of big data. For this purpose, appropriate techniques should be developed for data preprocessing in order to covert data into an appropriate form for learning. The existing frameworks should also be employed for parallel and distributed processes so that SVMs can be made scalable and properly online to be able to handle big data.

show abstract