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This paper compares the performance of linear and nonlinear kernels of Support Vector Machines (SVM) used for text classification. The study is motivated by the previous viewpoint that linear SVM performs better than nonlinear one, and that, although there are many investigations have proved that SVM performs well in text classification, there is no serious investigation on the comparison between...
This paper proposes a research work done in search of best-supervised learning algorithm and the best kernel for Hyperspectral Image classification. In this work, we find that SVM outperforms other supervised algorithms. Many kernels are utilized in support vector machines for classification. Among them Linear, Polynomial and RBF kernels are analysed and the kernel that best suits for the application...
This paper proposes a novel approach to improve the kernel-based word sense disambiguation (WSD). We first explain why linear kernels are more suitable to WSD and many other natural language processing problems than translation-invariant kernels. Based on the linear kernel, two external knowledge sources are integrated. One comprises a set of linguistic rules to find the crucial features. For the...
Post-beamforming second order Volterra filter (SOVF) was previously introduced for decomposing the pulse echo ultrasonic radio-frequency (RF) signal into its linear and quadratic components. Using singular value decomposition (SVD), an optimal minimum-norm least squares algorithm for deriving the coefficients of the linear and quadratic kernels of the SOVF was developed and verified. The ldquoseparablerdquo...
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