The nature of statistical learning theory
The nature of statistical learning theory
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
The Journal of Machine Learning Research
Sparseness of support vector machines
The Journal of Machine Learning Research
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
Predictive low-rank decomposition for kernel methods
ICML '05 Proceedings of the 22nd international conference on Machine learning
Demonstrating the stability of support vector machines for classification
Signal Processing - Signal processing in UWB communications
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
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Stability and bias-variance analysis are two powerful tools to better understand learning algorithms. We use these tools to analyze a class of support vector machines (SVMs) that try to reduce classifier complexity. The motivation for doing this is to compare the original and modified SVMs on two behavioral dimensions (a) stability and (b) learning behavior. Our preliminary experimental results show that (i) the class of algorithms which reduce classifier complexity by reducing the number of support vectors (SVs) are potentially unstable and (ii) the learning behavior is quite similar to the original SVMs.