Saturated Perceptrons for Maximum Margin and Minimum Misclassification Error
Neural Processing Letters
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Advantages of Unbiased Support Vector Classifiers for Data Mining Applications
Journal of VLSI Signal Processing Systems
Adapted user-dependent multimodal biometric authentication exploiting general information
Pattern Recognition Letters
Letters: Support vector perceptrons
Neurocomputing
Robust ASR using Support Vector Machines
Speech Communication
Letters: Support vector machine interpretation
Neurocomputing
Letters: Compact multi-class support vector machine
Neurocomputing
Separating hypersurfaces of SVMs in input spaces
Pattern Recognition Letters
Reducing the run-time complexity of support vector data descriptions
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Image Denoising with Kernels Based on Natural Image Relations
The Journal of Machine Learning Research
SVMs for automatic speech recognition: a survey
Progress in nonlinear speech processing
Fast support vector data descriptions for novelty detection
IEEE Transactions on Neural Networks
Multi-kernel growing support vector regressor
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
A unified SVM framework for signal estimation
Digital Signal Processing
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An iterative block training method for support vector classifiers (SVCs) based on weighted least squares (WLS) optimization is presented. The algorithm, which minimizes structural risk in the primal space, is applicable to both linear and nonlinear machines. In some nonlinear cases, it is necessary to previously find a projection of data onto an intermediate-dimensional space by means of either principal component analysis or clustering techniques. The proposed approach yields very compact machines, the complexity reduction with respect to the SVC solution is especially notable in problems with highly overlapped classes. Furthermore, the formulation in terms of WLS minimization makes the development of adaptive SVCs straightforward, opening up new fields of application for this type of model, mainly online processing of large amounts of (static/stationary) data, as well as online update in nonstationary scenarios (adaptive solutions). The performance of this new type of algorithm is analyzed by means of several simulations