optimal kernel
Empirical Optimal Kernel for Convex Multiple Kernel Learning
... Empirical Optimal Kernel and the corresponding algorithms based on it are effective for multiple features combination ...active kernel number and support vector number, the proposed methods achieve ...
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Adaptive Optimal Kernel Smooth-Windowed Wigner-Ville Distribution for Digital Communication Signal
... smoothing kernel, window, or filter ...an optimal distri- bution should have reasonable cross-terms suppression and minimal smearing of the ...the optimal kernel is signal-dependant [2, 3, ...
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Optimal Kernel Marginal Fisher Analysis for Face Recognition
... As for face recognition, classifier selection is another key issue after facial feature extraction. At present, the nearest neighbor (KNN) algorithm is one of the most widely used classifier algorithms. However, for ...
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Online Full Text
... The optimal kernel parameters of the CWD for voltage variation, waveform distortion and transient signals are at = ...its optimal kernel, the MLW and SCR are ...
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Power Quality Analysis Using Bilinear Time-Frequency Distributions
... a kernel that will produce the desired autoterm shape and an optimal kernel with respect to the autoterm quality and cross-term were demon- ...
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Protein Protein Interaction Extraction Based on Convex Combination Kernel Function
... single kernel functions, and only using single kernel function hardly trained the optimal classified model to extract PPI, this paper presents a strategy to find the optimal kernel ...
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Learning Translation Invariant Kernels for Classification
... the kernel function, which implicitly defines the feature space of an algo- rithm, has a crucial role in the success of kernel ...a kernel function over the class of translation invariant kernels for ...
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OPTIMIZATION OF HIGH VOLTAGE POWER SUPPLY FOR INDUSTRIAL MICROWAVE GENERATORS FOR ONE MAGNETRON
... the kernel-based learning algorithms, such as support vector domain description, depends heavily on the proper choice of the kernel ...the kernel machines to work on the optimal kernel ...
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Optimal Nonparametric Covariance Function Estimation for Any Family of Nonstationary Random Processes
... a kernel function. Di ff erent kernel functions have been proposed, ...one kernel over another, some prior knowledge about the random process is ...MSE optimal kernel function for any ...
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Representing Data Distributions with a Nonparametric Kernel Density: The Way to Estimate the Optimal Oil Contents of Palm Mesocarp at Various Periods
... is kernel density ...to kernel optimality process. We proposed Epanechnikov kernel which is the most optimal kernel in the ...Epanechnikov kernel simulation to estimate the ...
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C-Support Vector Classification the Estimation of the MS Subgroups Classification with Selected Kernels and Parameters
... Function kernel (RBF), Polynomial kernel, Sigmoid kernel and Linear kernel, four of the kernel types of C-SVC algorithm, the accuracy rates of MS subgroups classification and the ...
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Effects of some kernel factors on palm kernel oil extraction using a screw press
... Palm kernel screw press (MS – 100) fabricated by Magnus (Nigeria) was used for the oil extraction. The press has a capacity of about 101.7 kg h -1 at a speed of 56 rpm, powered by a 45 kW, 3 phase electric motor. ...
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Kernel Methods for Activation Energy Prediction
... Tanimoto kernel and marginalized graph kernel are both computed based on detection of linear features of ...marginalized kernel is the phenomenon of “tottering”, that is, the kernel counts ...
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Kernelizing Semantic Similarity Measurement Using Bi directional Learning Ranking for Cross Modal Retrieval
... This paper starts with the problem of cross-media information retrieval, analyzes the drawbacks of some existing classical retrieval methods, and compare different similarity measurement methods based on bidirectional ...
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Forecasting modeling with kernel function integration in gaussian processes
... the kernel of the quadratic algorithm, which is a function of complex change, but is slowly changing, and because of the fact that the time series data of one variable may consist of only two ...one Kernel ...
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Density and Risk Function of the Circular Kernel Study
... circular kernel. In addition, we determined the optimal window b ∗ n of this estimator after having first established the mean square error (MSE) and mean integrated squared error (MISE) which are necessary ...
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Identification of nonlinear systems using generalized kernel models
... generalized kernel regression model struc- ture. As with the standard kernel model, the kernel mean vectors are directly placed on the training input ...generalized kernel model has an ...
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Estimation of the applicability domain of kernel-based machine learning models for virtual screening
... section because a compound has to be very similar to the training samples to be retained in the innermost AD, but obviously has a structure-activity relationship not covered by the model. Thus, to compare the perfor- ...
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Optimal Feature Level Fusion Based IRIS and Fingerprint Multimodal Biometric System using Improved Multi Kernel SVM
... Multi Kernel Support vector machine (IMKSVM is ...hybrid kernel which incredibly improves the classification task of segregating the training ...hybrid kernel, the SVMs gainfully achieve the ...
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Forecast of fund volatility using least squares wavelet support vector regression machines
... wavelet kernel functions, the paper demonstrates the validity of LS-WSVR for fund volatility ...wavelet kernel functions outperforms the LS-SVR with Gaussian kernel function for in-sample and ...
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