Abstract:
In this paper, a novel approach is proposed as a new fast Support Vector Machines (SVM) basing on sequential minimal optimization(SMO), minimum enclosing ball(MEB) approa...Show MoreMetadata
Abstract:
In this paper, a novel approach is proposed as a new fast Support Vector Machines (SVM) basing on sequential minimal optimization(SMO), minimum enclosing ball(MEB) approach and active set strategy. The combination with these 3 techniques largely accelerates the training process of SVM, attains fewer support vectors(SVs) as well as obtains a acceptable accuracy comparing to original SVM. From simulation results, it is stated that the proposed method will be a good alternative for classification of large scale data.
Date of Conference: 28-30 October 2014
Date Added to IEEE Xplore: 26 January 2015
Electronic ISBN:978-1-4799-6541-0
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Support Vector Machine ,
- Large-scale Data ,
- Sequential Minimal Optimization ,
- Simulation Results ,
- Active Strategies ,
- Optimal Sequence ,
- Time And Space ,
- Large Datasets ,
- Training Time ,
- Efficient Algorithm ,
- Large-scale Datasets ,
- Experimental Environment ,
- Information In Order ,
- Quadratic Programming ,
- Decomposition Approach ,
- Geometric Approach ,
- Quadratic Programming Problem
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Support Vector Machine ,
- Large-scale Data ,
- Sequential Minimal Optimization ,
- Simulation Results ,
- Active Strategies ,
- Optimal Sequence ,
- Time And Space ,
- Large Datasets ,
- Training Time ,
- Efficient Algorithm ,
- Large-scale Datasets ,
- Experimental Environment ,
- Information In Order ,
- Quadratic Programming ,
- Decomposition Approach ,
- Geometric Approach ,
- Quadratic Programming Problem
