Browsing by Subject "Machine learning."
Now showing items 1-6 of 6
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Incremental kernel learning algorithms and applications.
(2006)Since the Support Vector Machines (SVMs) were introduced in 1995, SVMs have been recognized as essential tools for pattern classification and function approximation. Numerous publications show that SVMs outperform other ... -
Learning from data with uncertainty: Robust multiclass kernel-based classifiers and regressors.
(2005)Motivated by the presence of uncertainty in real data, in this research we investigate a robust optimization approach applied to multiclass support vector machines (SVMs) and support vector regression. Two new kernel ... -
Least square multi-class kernel machines with prior knowledge and applications.
(2006)In this study, the problem of discriminating between objects of two or more classes with (or without) prior knowledge is investigated. We present how a two-class discrimination model with or without prior knowledge can be ... -
Optimization issues in data analysis: An analytic center approach to kernel methods.
(2001)Support vector machines have recently attracted much attention in the machine learning and optimization communities for their remarkable generalization ability. The support vector machine solution corresponds to the center ... -
Robust optimization in support-vector machines and applications.
(2002)The main objective of this work is to investigate the robustness and stability of the behavior of the solutions of the Support Vector Machines model under bounded perturbations of the input data in the feature space. The ... -
Uncertainty and sensitivity analysis in support vector machines: Robust optimization and uncertain programming approaches.
(2006)Noisy or uncertain data are common in machine learning and data mining applications. Noisy data can significantly affect the behavior of data mining and machine learning algorithms. Robust optimization and sensitivity ...