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In this paper, the maximum correntropy (MC) criterion is used as the cost function in the online sequential extreme learning machine (OS-ELM) algorithm and constraint OS-ELM (COS-ELM) algorithm, generating the proposed OS-ELM based on maximum correntropy (OS-ELM-MC) and COS-ELM based on maximum correntropy (COS-ELM-MC). In comparison with OS-ELM and COS-ELM, the proposed OS-ELM-MC and COS-ELM-MC present...
The Least Squares Support Vector Machine (LS-SVM) is a modified SVM with a ridge regression cost function and equality constraints. It has been successfully applied in many classification problems. But, the common issue for LS-SVM is that it lacks sparseness, which is a serious drawback in its applications. To tackle this problem, a fast approach is proposed in this paper for developing sparse LS-SVM...
Recently, there has been a growing interest in learning distances directly from training data. While the previous works focused mainly on adapting distance measures over vectorial data, it is a well-known fact that many real-world data could not be easily represented as fixed length tuples of constants. In this paper we address this limitation and propose a novel class of distance learning techniques...
This paper establishes a new paradigm for convexly constrained adaptive learning in reproducing kernel Hilbert spaces (RKHS). Although the technique is of a general nature, we present it in the context of the beamforming problem. A priori knowledge, like beampattern specifications and constraints concerning robustness against steering vector errors, takes the form of multiple closed convex sets in...
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