Name
Title | ||
|---|---|---|
Support Vector classification for large data sets by reducing training data with change of classes |
Abstract | ||
|---|---|---|
In recent years support vector machines (SVM) has received considerable attention due to its high generalization ability and performance for a wide range of applications. However, the most important problem of this method is slow training for classification problems with a large data sets because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. This paper presents a novel SVM classification approach for large data sets by reducing training data and train the support vector machine using only these data. In this algorithm, a first stage uses SVM classification on a small data set in order to gets a sketch of classes distribution and labels the support vectors as a data set with label +1 and the other points as a data set with label -1. We call this change of classes. Then the algorithm obtains the classification hyperplane and classify the original input data set, the data points obtained with label +1 constitute the data points in the boundary of each original class and represent the most important data points, these data points are used as training data for a posterior SVM classification. The effectiveness of the approach proposed is supported by experimental results. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICSMC.2008.4811689 | Singapore |
Keywords | Field | DocType |
data reduction,pattern classification,support vector machines,SVM classification,classification problems,support vector classification,training data reduction | Structured support vector machine,Data mining,Data set,Small data,Computer science,Artificial intelligence,Hyperplane,Kernel (linear algebra),Data point,Pattern recognition,Support vector machine,Machine learning,Data reduction | Conference |
ISSN | ISBN | Citations |
1062-922X E-ISBN : 978-1-4244-2384-2 | 978-1-4244-2384-2 | 7 |
PageRank | References | Authors |
0.64 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jair Cervantes | 1 | 10 | 1.02 |
| Xiaoou Li | 2 | 42 | 4.20 |
| Wen Yu | 3 | 283 | 22.70 |