Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Solving the quadratic programming problem arising in support vector classification
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Automatically tuned linear algebra software
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Interactive supercomputing
Support Vector Optimization through Hybrids: Heuristics and Math Approach
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
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Support Vector Machines (SVMs) are of current interest in the solution of classification problems. However, serious challenges appear in the training problem when the training set is large. Training SVMs involves solving a linearly constrained quadratic programming problem. In this paper, we present a fast and easy-to-implement projected Conjugate Gradient algorithm for solving this quadratic programming problem.Compared with the exiting ones, this algorithm tries to be adaptive to each training problem and each computer's memory hierarchy. Although written in a high-level programming language, numerical experiments show that the performance of its MATLAB implementation is competitive with that of benchmark C/C++ codes such as SVMlight and SvmFu. The parallelism of this algorithm is also discussed in this paper.