The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Geometry and invariance in kernel based methods
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalisation Error Bounds for Sparse Linear Classifiers
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Exact simplification of support vector solutions
The Journal of Machine Learning Research
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
Level Set Approaches and Adaptive Asymmetrical SVMs Applied for Nonideal Iris Recognition
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Iris Recognition in Nonideal Situations
ISC '09 Proceedings of the 12th International Conference on Information Security
A hybrid optimization strategy for simplifying the solutions of support vector machines
Pattern Recognition Letters
A hybrid SVM based decision tree
Pattern Recognition
Multikernel semiparametric linear programming support vector regression
Expert Systems with Applications: An International Journal
Fast Multiclass SVM Classification Using Decision Tree Based One-Against-All Method
Neural Processing Letters
Engineering Applications of Artificial Intelligence
Online independent reduced least squares support vector regression
Information Sciences: an International Journal
Inverse matrix-free incremental proximal support vector machine
Decision Support Systems
A sequential algorithm for sparse support vector classifiers
Pattern Recognition
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SVM has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. Unfortunately, SVM is currently considerably slower in test phase caused by number of the support vectors, which has been a serious limitation for some applications. To overcome this problem, we proposed an adaptive algorithm named feature vectors selection (FVS) to select the feature vectors from the support vector solutions, which is based on the vector correlation principle and greedy algorithm. Through the adaptive algorithm, the sparsity of solution is improved and the time cost in testing is reduced. To select the number of the feature vectors adaptively by the requirements, the generalization and complexity trade-off can be directly controlled. The computer simulations on regression estimation and pattern recognition show that FVS is a promising algorithm to simplify the solution for support vector machine.