Choosing Multiple Parameters for Support Vector Machines
TLDR
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters.Abstract:
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.read more
Citations
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Journal ArticleDOI
Quadratic-radial-basis-function-kernel for classifying multi-class agricultural datasets with continuous attributes
TL;DR: A hybrid kernel based support vector machine (H-SVM) is proposed for classifying multi-class agricultural datasets having continuous attributes and it reveals a significant performance improvement over state of the art methods such as NB, k-NN, and SVM in terms of performance metrics such as accuracy, sensitivity, specificity, precision, and F-score.
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Fault Diagnosis for PEMFC Systems in Consideration of Dynamic Behaviors and Spatial Inhomogeneity
TL;DR: The individual cell voltages measured in a sliding diagnosis window are considered integrally as a diagnostic observation and a time-series analysis tool, named shapelet transform, is used to extract the discriminative features from the diagnostic observations.
Journal ArticleDOI
Two Criteria for Model Selection in Multiclass Support Vector Machines
Lei Wang,Ping Xue,Kap Luk Chan +2 more
TL;DR: Two model selection criteria by combining or redefining the radius-margin bound used in binary SVMs are developed, which give rise to comparable performance with much less computational overhead, particularly when a large number of model parameters are to be optimized.
Journal ArticleDOI
Building sparse representations and structure determination on LS-SVM substrates
TL;DR: A new method to obtain sparseness and structure detection for a class of kernel machines related to least-squares support vector machines (LS-SVMs) by adopting an hierarchical modeling strategy.
Proceedings ArticleDOI
The anisotropic Gaussian kernel for SVM classification of HRCT images of the lung
Alena Shamsheyeva,Arcot Sowmya +1 more
TL;DR: This work interpret lung patterns as textures and develop a texture classification technique for segmentation of lung patterns and compares the performance of isotropic and anisotropic Gaussian kernels and the applicability of the radius/margin bound to tuning parameters of the SVM algorithm on the problem of lung pattern classification.
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