Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Properties of support vector machines
Neural Computation
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Hierarchical Feature Extraction for Image Recognition
Journal of VLSI Signal Processing Systems
Image coding by fitting RBF-surfaces to subimages
Pattern Recognition Letters
Multi-Class SVM Classifier Based on Pairwise Coupling
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Incorporating multiple SVMs for automatic image annotation
Pattern Recognition
Machine-Learning-Based image categorization
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Multi-dimensional color histograms for segmentation of wounds in images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Image retrieval using transaction-based and SVM-based learning in relevance feedback sessions
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.