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linear svm and linear epsilon svr models  (MathWorks Inc)


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    MathWorks Inc linear svm and linear epsilon svr models
    ROC and accuracy for the gender classification task. (A) ROC curve across 10 testing folds for <t>the</t> <t>CNN</t> + LSTM and the <t>SVM</t> model. (B) Gender classification accuracy averaged across 10 testing folds. Bars refer to mean accuracy of all testing folds. Error bars indicate the standard error. Obviously, the CNN + LSTM model is statistically better than the SVM model (*** p < 0.001). (C) Accuracies of gender classification using the CNN + LSTM model on the real BOLD signals and their surrogate copies. A total of 100 surrogate data were generated by using MVPR to estimate the null distribution of classification accuracies (see section “Materials and Methods” for detail). With the mean classification accuracies as the statistic, results reveal that the classifier learned the connection dynamics with a probability of being wrong of <0.001. (D) The learning curves while training the CNN + LSTM model.
    Linear Svm And Linear Epsilon Svr Models, 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/result/linear svm and linear epsilon svr models/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    linear svm and linear epsilon svr models - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction"

    Article Title: A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction

    Journal: Frontiers in Neuroscience

    doi: 10.3389/fnins.2020.00881

    ROC and accuracy for the gender classification task. (A) ROC curve across 10 testing folds for the CNN + LSTM and the SVM model. (B) Gender classification accuracy averaged across 10 testing folds. Bars refer to mean accuracy of all testing folds. Error bars indicate the standard error. Obviously, the CNN + LSTM model is statistically better than the SVM model (*** p < 0.001). (C) Accuracies of gender classification using the CNN + LSTM model on the real BOLD signals and their surrogate copies. A total of 100 surrogate data were generated by using MVPR to estimate the null distribution of classification accuracies (see section “Materials and Methods” for detail). With the mean classification accuracies as the statistic, results reveal that the classifier learned the connection dynamics with a probability of being wrong of <0.001. (D) The learning curves while training the CNN + LSTM model.
    Figure Legend Snippet: ROC and accuracy for the gender classification task. (A) ROC curve across 10 testing folds for the CNN + LSTM and the SVM model. (B) Gender classification accuracy averaged across 10 testing folds. Bars refer to mean accuracy of all testing folds. Error bars indicate the standard error. Obviously, the CNN + LSTM model is statistically better than the SVM model (*** p < 0.001). (C) Accuracies of gender classification using the CNN + LSTM model on the real BOLD signals and their surrogate copies. A total of 100 surrogate data were generated by using MVPR to estimate the null distribution of classification accuracies (see section “Materials and Methods” for detail). With the mean classification accuracies as the statistic, results reveal that the classifier learned the connection dynamics with a probability of being wrong of <0.001. (D) The learning curves while training the CNN + LSTM model.

    Techniques Used: Generated

    Prediction performance of fluid intelligence and crystallized intelligence. (A) The correlations between predicted and observed intelligence scores for the CNN + LSTM and support vector machine (SVM) models. Note that the CNN + LSTM model exhibits the highest correlation scores for both tasks. Each subject is represented by one dot, and 95% confidence interval for the best-fit line is represented by the gray area which is used to assess the predictive power of the model. (B) Comparison between mean MAE across 10 testing folds for the CNN + LSTM and the SVM model. Lower is better. Bars refer to the mean accuracy of all testing folds, and error bars refer to their standard error. Note that the CNN + LSTM model is statistically better than the SVM model (* p < 0.05).
    Figure Legend Snippet: Prediction performance of fluid intelligence and crystallized intelligence. (A) The correlations between predicted and observed intelligence scores for the CNN + LSTM and support vector machine (SVM) models. Note that the CNN + LSTM model exhibits the highest correlation scores for both tasks. Each subject is represented by one dot, and 95% confidence interval for the best-fit line is represented by the gray area which is used to assess the predictive power of the model. (B) Comparison between mean MAE across 10 testing folds for the CNN + LSTM and the SVM model. Lower is better. Bars refer to the mean accuracy of all testing folds, and error bars refer to their standard error. Note that the CNN + LSTM model is statistically better than the SVM model (* p < 0.05).

    Techniques Used: Plasmid Preparation, Comparison

    Model performance of rs-fMRI based gender classification and intelligence prediction tasks in some recent studies.
    Figure Legend Snippet: Model performance of rs-fMRI based gender classification and intelligence prediction tasks in some recent studies.

    Techniques Used: Surround Optical-fiber Immunoassay



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    linear svm and linear epsilon svr models  (MathWorks Inc)
    90
    MathWorks Inc linear svm and linear epsilon svr models
    ROC and accuracy for the gender classification task. (A) ROC curve across 10 testing folds for <t>the</t> <t>CNN</t> + LSTM and the <t>SVM</t> model. (B) Gender classification accuracy averaged across 10 testing folds. Bars refer to mean accuracy of all testing folds. Error bars indicate the standard error. Obviously, the CNN + LSTM model is statistically better than the SVM model (*** p < 0.001). (C) Accuracies of gender classification using the CNN + LSTM model on the real BOLD signals and their surrogate copies. A total of 100 surrogate data were generated by using MVPR to estimate the null distribution of classification accuracies (see section “Materials and Methods” for detail). With the mean classification accuracies as the statistic, results reveal that the classifier learned the connection dynamics with a probability of being wrong of <0.001. (D) The learning curves while training the CNN + LSTM model.
    Linear Svm And Linear Epsilon Svr Models, 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/result/linear svm and linear epsilon svr models/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    linear svm and linear epsilon svr models - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    ROC and accuracy for the gender classification task. (A) ROC curve across 10 testing folds for the CNN + LSTM and the SVM model. (B) Gender classification accuracy averaged across 10 testing folds. Bars refer to mean accuracy of all testing folds. Error bars indicate the standard error. Obviously, the CNN + LSTM model is statistically better than the SVM model (*** p < 0.001). (C) Accuracies of gender classification using the CNN + LSTM model on the real BOLD signals and their surrogate copies. A total of 100 surrogate data were generated by using MVPR to estimate the null distribution of classification accuracies (see section “Materials and Methods” for detail). With the mean classification accuracies as the statistic, results reveal that the classifier learned the connection dynamics with a probability of being wrong of <0.001. (D) The learning curves while training the CNN + LSTM model.

    Journal: Frontiers in Neuroscience

    Article Title: A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction

    doi: 10.3389/fnins.2020.00881

    Figure Lengend Snippet: ROC and accuracy for the gender classification task. (A) ROC curve across 10 testing folds for the CNN + LSTM and the SVM model. (B) Gender classification accuracy averaged across 10 testing folds. Bars refer to mean accuracy of all testing folds. Error bars indicate the standard error. Obviously, the CNN + LSTM model is statistically better than the SVM model (*** p < 0.001). (C) Accuracies of gender classification using the CNN + LSTM model on the real BOLD signals and their surrogate copies. A total of 100 surrogate data were generated by using MVPR to estimate the null distribution of classification accuracies (see section “Materials and Methods” for detail). With the mean classification accuracies as the statistic, results reveal that the classifier learned the connection dynamics with a probability of being wrong of <0.001. (D) The learning curves while training the CNN + LSTM model.

    Article Snippet: For comparing with the CNN + LSTM model, we used linear SVM and linear epsilon SVR models (LIBSVM toolbox in Matlab ) based on dynamic characteristic of dFC (dFC-Str, which refers to the overall strength of dFC) , to achieve gender classification and intelligence prediction with the same 10-fold cross-validation strategies.

    Techniques: Generated

    Prediction performance of fluid intelligence and crystallized intelligence. (A) The correlations between predicted and observed intelligence scores for the CNN + LSTM and support vector machine (SVM) models. Note that the CNN + LSTM model exhibits the highest correlation scores for both tasks. Each subject is represented by one dot, and 95% confidence interval for the best-fit line is represented by the gray area which is used to assess the predictive power of the model. (B) Comparison between mean MAE across 10 testing folds for the CNN + LSTM and the SVM model. Lower is better. Bars refer to the mean accuracy of all testing folds, and error bars refer to their standard error. Note that the CNN + LSTM model is statistically better than the SVM model (* p < 0.05).

    Journal: Frontiers in Neuroscience

    Article Title: A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction

    doi: 10.3389/fnins.2020.00881

    Figure Lengend Snippet: Prediction performance of fluid intelligence and crystallized intelligence. (A) The correlations between predicted and observed intelligence scores for the CNN + LSTM and support vector machine (SVM) models. Note that the CNN + LSTM model exhibits the highest correlation scores for both tasks. Each subject is represented by one dot, and 95% confidence interval for the best-fit line is represented by the gray area which is used to assess the predictive power of the model. (B) Comparison between mean MAE across 10 testing folds for the CNN + LSTM and the SVM model. Lower is better. Bars refer to the mean accuracy of all testing folds, and error bars refer to their standard error. Note that the CNN + LSTM model is statistically better than the SVM model (* p < 0.05).

    Article Snippet: For comparing with the CNN + LSTM model, we used linear SVM and linear epsilon SVR models (LIBSVM toolbox in Matlab ) based on dynamic characteristic of dFC (dFC-Str, which refers to the overall strength of dFC) , to achieve gender classification and intelligence prediction with the same 10-fold cross-validation strategies.

    Techniques: Plasmid Preparation, Comparison

    Model performance of rs-fMRI based gender classification and intelligence prediction tasks in some recent studies.

    Journal: Frontiers in Neuroscience

    Article Title: A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction

    doi: 10.3389/fnins.2020.00881

    Figure Lengend Snippet: Model performance of rs-fMRI based gender classification and intelligence prediction tasks in some recent studies.

    Article Snippet: For comparing with the CNN + LSTM model, we used linear SVM and linear epsilon SVR models (LIBSVM toolbox in Matlab ) based on dynamic characteristic of dFC (dFC-Str, which refers to the overall strength of dFC) , to achieve gender classification and intelligence prediction with the same 10-fold cross-validation strategies.

    Techniques: Surround Optical-fiber Immunoassay