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linear discriminant analysis model  (MathWorks Inc)


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    MathWorks Inc linear discriminant analysis model
    Analysis of receiver operating characteristic curves for different classification models. *, P<0.05. SVM, support vector machine; AUC, area under the curve; CI, confidence interval; KNN, <t>K-nearest</t> <t>neighbor;</t> LR, logistic regression; <t>LDA,</t> linear discriminant analysis.
    Linear Discriminant Analysis Model, 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/linear+discriminant+model/pmc11485364-70-9-26
    Average 90 stars, based on 1 article reviews
    linear discriminant analysis model - by Bioz Stars, 2026-09
    90/100 stars

    Images

    1) Product Images from "Support vector machine classification of irritable bowel syndrome patients based on whole-brain resting-state functional connectivity features"

    Article Title: Support vector machine classification of irritable bowel syndrome patients based on whole-brain resting-state functional connectivity features

    Journal: Quantitative Imaging in Medicine and Surgery

    doi: 10.21037/qims-24-892

    Analysis of receiver operating characteristic curves for different classification models. *, P<0.05. SVM, support vector machine; AUC, area under the curve; CI, confidence interval; KNN, K-nearest neighbor; LR, logistic regression; LDA, linear discriminant analysis.
    Figure Legend Snippet: Analysis of receiver operating characteristic curves for different classification models. *, P<0.05. SVM, support vector machine; AUC, area under the curve; CI, confidence interval; KNN, K-nearest neighbor; LR, logistic regression; LDA, linear discriminant analysis.

    Techniques Used: Plasmid Preparation

    Related Articles

    Whisker Assay:

    Article Title: Motor cortex gates distractor stimulus encoding in sensory cortex.
    Article Snippet: .. We trained a linear discriminant model (MATLAB function: fitcdiscr, with diagonal covariance matrix) to classify the spike rates of target-aligned S1 neurons into target or distractor whisker stimulus trial types. ..

    other:

    Article Title: Nanotunnels within Poly(3,4-ethylenedioxythiophene)-Carbon Nanotube Composite for Highly Sensitive Neural Interfacing.
    Article Snippet: Neural electrodes are developed for direct communication with neural tissues for theranostics.. Although various strategies have been employed to improve performance, creating an intimate electrode−tissue interface with high electrical fidelity remains a great challenge.. Here, we report the rational design of a tunnel-like electrode coating comprising poly(3,4-ethylenedioxythiophene) (PEDOT) and carbon nanotubes (CNTs) for highly sensitive neural recording.

    Derivative Assay:

    Article Title: Conformational fingerprinting with Raman spectroscopy reveals protein structure as a translational biomarker of muscle pathology
    Article Snippet: Mode coefficients (weights) were compared using unpaired t -tests (GraphPad Prism, version 9). .. For classification, the NMF weights were fed into a linear discriminant model and performance statistics derived through a 10-fold cross validation with stratification, which preserves the balance of classes across the folds (MATLAB, 2023a). ..

    Biomarker Discovery:

    Article Title: Conformational fingerprinting with Raman spectroscopy reveals protein structure as a translational biomarker of muscle pathology
    Article Snippet: Mode coefficients (weights) were compared using unpaired t -tests (GraphPad Prism, version 9). .. For classification, the NMF weights were fed into a linear discriminant model and performance statistics derived through a 10-fold cross validation with stratification, which preserves the balance of classes across the folds (MATLAB, 2023a). ..

    Article Title: Motor-related signals support localization invariance for stable visual perception
    Article Snippet: .. On the first three PCs, we fitted a linear discriminant model (fitcdiscr.m Matlab function) with five-fold cross validation. ..

    Construct:

    Article Title: Neural dynamics in the limbic system during male social behaviors.
    Article Snippet: .. A linear discriminant model for each recording session was constructed using the MATLAB function "fitcdiscr "using 80% of randomly selected data (training data). .. Themodel was then used to predict the behaviors associated with the remaining 20% (testing data) of the Ca2+ recording data in each session using MATLAB function "predict".



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    Image Search Results


    Analysis of receiver operating characteristic curves for different classification models. *, P<0.05. SVM, support vector machine; AUC, area under the curve; CI, confidence interval; KNN, K-nearest neighbor; LR, logistic regression; LDA, linear discriminant analysis.

    Journal: Quantitative Imaging in Medicine and Surgery

    Article Title: Support vector machine classification of irritable bowel syndrome patients based on whole-brain resting-state functional connectivity features

    doi: 10.21037/qims-24-892

    Figure Lengend Snippet: Analysis of receiver operating characteristic curves for different classification models. *, P<0.05. SVM, support vector machine; AUC, area under the curve; CI, confidence interval; KNN, K-nearest neighbor; LR, logistic regression; LDA, linear discriminant analysis.

    Article Snippet: In this study, machine learning models based on linear discriminant analysis (LDA), logistic regression (LR), K-nearest neighbor (KNN), and support vector machine (SVM) models accomplished by MATLAB were used to classify the IBS patients and HCs.

    Techniques: Plasmid Preparation