Journal Article10.1109/TNN.2009.2014229
Building Sparse Multiple-Kernel SVM Classifiers
112
TL;DR: Experiments on a large number of toy and real-world data sets show that the resultant classifier is compact and accurate, and can also be easily trained by simply alternating linear program and standard SVM solver.
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Abstract: The support vector machines (SVMs) have been very successful in many machine learning problems. However, they can be slow during testing because of the possibly large number of support vectors obtained. Recently, Wu (2005) proposed a sparse formulation that restricts the SVM to use a small number of expansion vectors. In this paper, we further extend this idea by integrating with techniques from multiple-kernel learning (MKL). The kernel function in this sparse SVM formulation no longer needs to be fixed but can be automatically learned as a linear combination of kernels. Two formulations of such sparse multiple-kernel classifiers are proposed. The first one is based on a convex combination of the given base kernels, while the second one uses a convex combination of the so-called ldquoequivalentrdquo kernels. Empirically, the second formulation is particularly competitive. Experiments on a large number of toy and real-world data sets show that the resultant classifier is compact and accurate, and can also be easily trained by simply alternating linear program and standard SVM solver.
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Citations
Classification of Categorical Data Based on the Chi-Square Dissimilarity and t-SNE
Luis Ariosto Serna Cardona,Hernán Darío Vargas-Cardona,Piedad Navarro González,David Augusto Cárdenas Peña,Álvaro Ángel Orozco Gutiérrez +4 more
- 04 Dec 2020
TL;DR: This work proposes an identification approach for categorical variables using conventional classifiers (LDC-QDC-KNN-SVM and different mapping techniques to increase the separability of classes using the Chi-square as a measure of dissimilarity.
A Novel Approach for Diagnosis of Analog Circuit Fault by Using GMKL-SVM and PSO
TL;DR: This paper presents a novel analog circuit fault diagnosis approach using generalized multiple kernel learning-support vector machine (GMKL-SVM) method and particle swarm optimization (PSO) algorithm that has higher diagnosis precision than the referenced methods.
An analog circuit fault diagnosis method based on DCQGA-SMKL-SVM
Xue-long YAN,Liu-qing GONG,Bin-bin WANG +2 more
TL;DR: A novel analog circuit fault diagnosis method is proposed, utilizing DCQGA-optimized SMKL-SVM to improve diagnosis accuracy, outperforming DCQGA-SVM, with a higher correct diagnosis rate for simulated circuits, including a double quadratic filter and a four-op-amp second-order high-pass filter.
SCIHTBB: Sparsity constrained iterative hard thresholding with Barzilai-Borwein step size
Zhipeng Xie,Songcan Chen +1 more
TL;DR: Experimental comparisons with some state of the art methods verify that SCIHTBB is faster and more accurate for compressive sensing and matrix completion.
Incorporation of Data-Mined Knowledge into Black-Box SVM for Interpretability
TL;DR: Experimental results on eight UCI datasets demonstrate the superiority of the proposed pTsm-SVM over the standard soft-margin SVM both in terms of accuracy and interpretability.
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