Improved SVM regression using mixtures of kernels

Smits, Guido; Jordaan, E.M.

doi:10.1109/ijcnn.2002.1007589

Cited by 185 publications

(114 citation statements)

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“…As was defined by Smits et al [10], an exemplary mixture of the RBF and polynomial kernels is given by…”

Section: Mixtures Of Kernelsmentioning

confidence: 99%

PSO-Least Squares SVM for Clustering in Cognitive Radio Sensor Networks

Martyna

2015

IFIP Advances in Information and Communication Technology

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Abstract. In this paper, a solution for a cluster formation in cognitive radio networks is presented. The solution features a network-wide energy consumption model for these networks. The particle swarm optimisation (PSO) and least squares support vector machines (LS-SVMs) have been transformed into our clustering problem. The obtained results show that the given hybrid AI system provides a good estimate of a cluster formation. Through extensive simulations, we observed that the PSO-LS-SVM method can be effectively used under various spectrum characteristics. Moreover, the formed clusters are reliable and stable in a dynamic frequency environment.

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“…As was defined by Smits et al [10], an exemplary mixture of the RBF and polynomial kernels is given by…”

Section: Mixtures Of Kernelsmentioning

confidence: 99%

PSO-Least Squares SVM for Clustering in Cognitive Radio Sensor Networks

Martyna

2015

IFIP Advances in Information and Communication Technology

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“…But they can be classified into two main types, that is local kernel and global kernels (Smits et al 2002).…”

Section: Compound Kernelsmentioning

confidence: 99%

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

Zhao

Gao

Liu

et al. 2010

Computer and Computing Technologies in Agriculture III

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Abstract. The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

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“…Kernel functions can be divided in two classes: local and global kernels [29]. Following [2] we define the locality of a kernel as: …”

Section: Local and Global Basic Kernelsmentioning

confidence: 99%

“…Local kernels and in particular the RBF kernel show very good classification capability but they can suffer from the curse of dimensionality problem [3] and they can fail with datasets that require long range extrapolation. An attempt to mix the good characteristics of local and global kernels is reported in [29] where RBF and polynomial kernels are considered for SVM regression.…”

Section: Introductionmentioning

confidence: 99%

“…Local kernels do not make use of prior information and when the distance between a test point and a training point tends to infinity the value of the kernel is constant and independent of the test point [2] [29]. A popular local kernel is the radial basis function (RBF) kernel that tends to zero for points whose distance is high with respect to a width parameter that regulates the degree of locality.…”

Section: Introductionmentioning

confidence: 99%

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Operators for transforming kernels into quasi-local kernels that improve SVM accuracy

Segata

Blanzieri

2010

J Intell Inf Syst

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In the field of statistical machine learning, the integration of kernel methods with local information has been proposed through locality-improved kernels for Support Vector Machines (SVM) that make use of prior information, local kernels and local SVM that apply the SVM approach only on the subset of points close to the testing one. Here we propose a novel family of operators on kernels able to integrate the local information into any kernel without prior information obtaining quasi-local kernels. The quasi-local kernels maintain the possibly global properties of the input kernel and they increase the kernel value as the points get closer in the feature space of the input kernel. The operators combine the input kernel with a locality-dependent term, and accept two parameters that regulate the width of the exponential influence of points in the locality-dependent term and the balancing between the two terms. Experiments carried out with data-dependent systematic selection of the parameters of the operators (i.e. without the need for model selection phase on the obtained kernels) on a total of 33 datasets with different characteristics and application domains, achieve very good results.

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Improved SVM regression using mixtures of kernels

Cited by 185 publications

References 1 publication

PSO-Least Squares SVM for Clustering in Cognitive Radio Sensor Networks

PSO-Least Squares SVM for Clustering in Cognitive Radio Sensor Networks

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

Operators for transforming kernels into quasi-local kernels that improve SVM accuracy

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