SVMV – a novel algorithm for the visualization of SVM classification results

Authors:
Xiaohong Wang;Sitao Wu;Xiaoru Wang;Qunzhan Li
Affiliations:
School of Electrical Engineering, Southwest Jiaotong University, Chengdu, P.R. China;School of Electrical Engineering, Southwest Jiaotong University, Chengdu, P.R. China;School of Electrical Engineering, Southwest Jiaotong University, Chengdu, P.R. China;School of Electrical Engineering, Southwest Jiaotong University, Chengdu, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 6
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Self-organizing maps

Self-organizing maps
Visualization of high-dimensional data with relational perspective map

Information Visualization
New Support Vector Algorithms

Neural Computation
A Nonlinear Mapping for Data Structure Analysis

IEEE Transactions on Computers
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets

IEEE Transactions on Neural Networks

Using nonlinear dimensionality reduction to visualize classifiers

IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel algorithm, called support vector machine visualization (SVMV), is proposed. The SVMV algorithm is based on support vector machine (SVM) and self-organizing mapping (SOM). High dimensional data and binary classification results can be visualized in a low dimensional space. Compared with other traditional visualization algorithms like SOM and Sammon’s mapping algorithm, the SVMV algorithm can deliver better visualization on classification results. Experimental results corroborate the effectiveness and usefulness of SVMV.