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non-linear binary classifier svm (c-svm) with a gaussian kernel  (MathWorks Inc)


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    MathWorks Inc non-linear binary classifier svm (c-svm) with a gaussian kernel
    Non Linear Binary Classifier Svm (C Svm) With A Gaussian 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/binary+svm+classifier/pm22510256-110-15-19
    Average 90 stars, based on 1 article reviews
    non-linear binary classifier svm (c-svm) with a gaussian kernel - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    other:

    Article Title: A comprehensive study of ultrasonic detection and damage quantification of barely visible impact damages in a carbon fiber reinforced polymer composite material
    Article Snippet: Funding information Verifi Technologies Abstract This work investigates detection and evaluation of barely visible impact damage (BVID) on a carbon fiber reinforced polymer composite using nondestructive evaluation.. Specifically, this paper presents a novel method to analyze full waveform from ultrasonic data captured from a new field portable inspection station.. Forty-eight samples are impacted between 8 and 16 J representing a spectrum of surface damage below the visual threshold to barely visible surface damage.

    Article Title: Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins
    Article Snippet: We also performed a kernel density estimation of the interdye distance distribution without explicitly accounting for linker broadening using a Gaussian kernel by the ksdensity function of MATLAB, which computes the theoretically optimal bandwidth for normally distributed data49.

    Article Title: Complementary cortical and striatal encoding of locomotor preparation and performance
    Article Snippet: This classification task employed a multiclass SVM with a Gaussian kernel, implemented via the LIBSVM library in MATLAB.

    Article Title: SHM System for Composite Material Based on Lamb Waves and Using Machine Learning on Hardware.
    Article Snippet: During the SVM training phase, the Gaussian kernel function outperformed other kernels, applied with a Kernel scale of 0.1 in Matlab R2024a using the fitcsvm() function.

    Article Title: Frontal noradrenergic and cholinergic transients exhibit distinct spatiotemporal dynamics during competitive decision-making.
    Article Snippet: For plotting example traces of fluorescent and pupil signals, we smoothed the ΔF/F(t) and pupil z- score traces using a Gaussian kernel with the MATLAB function smooth.

    Article Title: Basal ganglia deep brain stimulation restores cognitive flexibility and exploration-exploitation balance disrupted by NMDA-R antagonism.
    Article Snippet: The obtained values were smoothed in all cases using a 50ms-wide Gaussian Kernel (MATLAB’s filtfilt function).

    Article Title: Fast face-selective responses in prefrontal face patches of the macaque
    Article Snippet: The spike trains were smoothed using a Gaussian kernel (σ = 10 ms) with MATLAB gaussfilt function.

    Standard Deviation:

    Article Title: Osteoarthritis leads to more contractile and protrusive chondrocytes when cultured in 3D degradable hydrogels
    Article Snippet: Colocalization of two metrics (actin-fibronectin and actin-pMLC) within a cell is reported as the correlation coefficient and is computed using the projected fluorescent z-stacks and the corr2 function in MATLAB. .. Prior to the computation, the respective fluorescent images are blurred using a Gaussian smoothing kernel with standard deviation of 0.5 (function imgaussfilt in MATLAB). ..



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    System description.

    Journal: Biomedical Informatics Insights

    Article Title: Sentiment Analysis of Suicide Notes: A Shared Task

    doi: 10.4137/BII.S9042

    Figure Lengend Snippet: System description.

    Article Snippet: National Research Council Canada , NRC , Word unigrams and bigrams, thesaurus matches, character 4-grams, document length, various sentence-level patterns , None , 71061 , 608448/(71061 * 4633) = 0.00185 , Feature vectors normalized to unit length , Binary SVM; one-classifier-per-label , None , 10-fold cross validation , 0.5522.

    Techniques: Selection, Biomarker Discovery, Labeling, Expressing, Generated, Sequencing