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svm-based regression model with a linear kernel  (MathWorks Inc)


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    MathWorks Inc svm-based regression model with a linear kernel
    Svm Based Regression Model With A Linear Kernel, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/kernel-based+svm/pm38177824-381-20-24
    Average 90 stars, based on 1 article reviews
    svm-based regression model with a linear kernel - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Investigation of the structure and dynamics of amorphous calcium carbonate by NMR: stabilization by poly-aspartate and comparison to monohydrocalcite.
    Article Snippet: For this purpose, we relied on the kernel of the MATLAB-based magnetic resonance simulation library Spinach.49 The evolution of the 13C magnetization in the x,yplane was simply observed for a given set of CSA parameters.

    Article Title: Modulatory effect of levodopa on the basal ganglia-cerebellum connectivity in Parkinson's disease.
    Article Snippet: Binary classifications of de novo and levodopa-medicated patients were performed using SVMs with the linear kernel through the fitcsvm route in MATLAB.

    Article Title: Numerical simulation of two-phase gas-liquid flowthrough horizontal annulus pipe
    Article Snippet: In this present work, the kernel smoothing density function of MATLAB was adopted to describe the PDF for the CFD flow patterns.

    Article Title: Changes in searching behaviour of CSL transcription complexes in Notch active conditions
    Article Snippet: Molecule density maps showing the spatial distribution of molecule trajectories were generated using kernel smoothing in MATLAB, with 4 pixel (440 nm) bandwidth ( ).

    Article Title: Implementation of MF block in CNN for advanced REB fault diagnosis.
    Article Snippet: In this study, we employed a linear kernel with automatic scaling and a box constraint set to 1, and processed in MATLAB.

    Article Title: Predicting party switching through machine learning and open data
    Article Snippet: The probability density functions of these features (considering the whole legislature, see ) in the Deputies groups were estimated via a kernel smoothing function estimate ( ksdensity in Matlab).

    Article Title: Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease.
    Article Snippet: Background: Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer’s disease (AD).. Purpose: To evaluate the sensitivity of FW-DTI in detecting white matter microstructural changes in AD.. To validate the effectiveness of FW-DTI indices to predict amyloid-beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes.

    Article Title: When neighbors become family: the dear-enemy effect of swimming crab and the verification of the formation hypothesis
    Article Snippet: 1 The Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, 5 Yushan Road, Qingdao 266003, Shandong, China 2 Function Laboratory for Marine Fisheries Science and Food Production Processes, Laoshan Laboratory, Qingdao 266237, Shandong, China 3 Marine Science Research Institute of Shandong Province (National Oceanographic Center, Qingdao), Qingdao 266104, China 4 School of Marine Science and Engineering, Qingdao Agricultural University, Qingdao 266109, Shandong, China Abstract The neighbor-stranger response difference, which serves as a fundamental social network relationship, plays a crucial role in establishing and expanding complex social networks.. However, the underlying causes of this phenomenon have received limited attention in controlled indoor settings.. This study focused on Portunus trituberculatus, a representative crustacean species in the Western Pacific Ocean.



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    The multistage learning approach and decision level fusion of individual classifiers. “Fusion 1” refers to the hard-level combination of the individual predictions obtained from RBF and Polynomial kernel based SVMs. “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM. “Fusion 3” refers to the hard-level combination of the individual predictions obtained from Softmax function and Polynomial kernel based SVM. “Fusion 4” refers to the hard-level combination of the individual predictions obtained from Softmax function, RBF and Polynomial kernel based SVMs

    Journal: Applied Intelligence

    Article Title: Decision and feature level fusion of deep features extracted from public COVID-19 data-sets

    doi: 10.1007/s10489-021-02945-8

    Figure Lengend Snippet: The multistage learning approach and decision level fusion of individual classifiers. “Fusion 1” refers to the hard-level combination of the individual predictions obtained from RBF and Polynomial kernel based SVMs. “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM. “Fusion 3” refers to the hard-level combination of the individual predictions obtained from Softmax function and Polynomial kernel based SVM. “Fusion 4” refers to the hard-level combination of the individual predictions obtained from Softmax function, RBF and Polynomial kernel based SVMs

    Article Snippet: “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM.

    Techniques:

    The detailed presentation of accuracy values obtained from applied individual and ensemble learning scenarios for three data-sets (average accuracy values of 5-folds are given)

    Journal: Applied Intelligence

    Article Title: Decision and feature level fusion of deep features extracted from public COVID-19 data-sets

    doi: 10.1007/s10489-021-02945-8

    Figure Lengend Snippet: The detailed presentation of accuracy values obtained from applied individual and ensemble learning scenarios for three data-sets (average accuracy values of 5-folds are given)

    Article Snippet: “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM.

    Techniques: Standard Deviation, Plasmid Preparation