Incremental Reduced Support Vector Machines

  title={Incremental Reduced Support Vector Machines},
  author={Yuh-Jye Lee and Hung-Yi Lo and Su-Yun Huang},
The reduced support vector machine (RSVM) has been proposed to avoid the computational difficulties in generating a nonlinear support vector machine classifier for a massive dataset. RSVM selects a small random subset from the entire dataset with a user pre-specified size m̄ to generate a reduced kernel (rectangular) matrix. This reduced kernel will replace the fully dense square kernel matrix used in the nonlinear support vector machine formulation to cut the problem size and computational… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.
Showing 1-9 of 9 extracted citations

A Passive-Aggressive Algorithm for Semi-supervised Learning

2010 International Conference on Technologies and Applications of Artificial Intelligence • 2010
View 9 Excerpts

Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression

IEEE Transactions on Knowledge and Data Engineering • 2009
View 1 Excerpt

Reduced Support Vector Machines: A Statistical Theory

IEEE Transactions on Neural Networks • 2007
View 1 Excerpt


Publications referenced by this paper.
Showing 1-10 of 13 references

Robust Linear and Support Vector Regression

IEEE Trans. Pattern Anal. Mach. Intell. • 2000
View 1 Excerpt

The Nature of Statistical Learning Theory

Statistics for Engineering and Information Science • 2000
View 2 Excerpts

A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery • 1998
View 2 Excerpts

Machine Learning

T. M. Mitchell
McGraw-Hill, Boston • 1997
View 1 Excerpt

Similar Papers

Loading similar papers…