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codes of implementation of lssvm  (MathWorks Inc)


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    MathWorks Inc codes of implementation of lssvm
    Flowchart of N-fold cross-validation method. Stage I: Dividing dataset into two sets: modeling (80 %) and test (20 %). Stage II: Calculation of ‘ c ’ and <t>‘</t> <t>σ</t> ’ coefficients via GA or simplex algorithms; II-I: Dividing dataset into two sets of training and validation through cross-validation method; II-II: Training of <t>LSSVM</t> per different ‘ c ’ and ‘ σ ’ coefficients; II-III: Test of trained LSSVM model and fitness function calculation ; II-IV: Iteration of stages II-I , II-II , and II-III for obtaining the best ‘ c ’ and ‘ σ ’ coefficients. Stage III: Training of LSSVM model using modeling dataset and calculation of modeling error. Stage IV: Test of LSSVM model using test dataset, and calculation of test error.
    Codes Of Implementation Of Lssvm, 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/codes+of+implementation+of+lssvm/pmc06371636-91-9-0?v=MathWorks+Inc
    Average 90 stars, based on 1 article reviews
    codes of implementation of lssvm - by Bioz Stars, 2026-07
    90/100 stars

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    1) Product Images from "A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling"

    Article Title: A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling

    Journal: Iranian Journal of Biotechnology

    doi: 10.21859/ijb.1542

    Flowchart of N-fold cross-validation method. Stage I: Dividing dataset into two sets: modeling (80 %) and test (20 %). Stage II: Calculation of ‘ c ’ and ‘ σ ’ coefficients via GA or simplex algorithms; II-I: Dividing dataset into two sets of training and validation through cross-validation method; II-II: Training of LSSVM per different ‘ c ’ and ‘ σ ’ coefficients; II-III: Test of trained LSSVM model and fitness function calculation ; II-IV: Iteration of stages II-I , II-II , and II-III for obtaining the best ‘ c ’ and ‘ σ ’ coefficients. Stage III: Training of LSSVM model using modeling dataset and calculation of modeling error. Stage IV: Test of LSSVM model using test dataset, and calculation of test error.
    Figure Legend Snippet: Flowchart of N-fold cross-validation method. Stage I: Dividing dataset into two sets: modeling (80 %) and test (20 %). Stage II: Calculation of ‘ c ’ and ‘ σ ’ coefficients via GA or simplex algorithms; II-I: Dividing dataset into two sets of training and validation through cross-validation method; II-II: Training of LSSVM per different ‘ c ’ and ‘ σ ’ coefficients; II-III: Test of trained LSSVM model and fitness function calculation ; II-IV: Iteration of stages II-I , II-II , and II-III for obtaining the best ‘ c ’ and ‘ σ ’ coefficients. Stage III: Training of LSSVM model using modeling dataset and calculation of modeling error. Stage IV: Test of LSSVM model using test dataset, and calculation of test error.

    Techniques Used: Biomarker Discovery

    LSSVM parameters over Simplex, GA and NSGAII optimization algorithms.
    Figure Legend Snippet: LSSVM parameters over Simplex, GA and NSGAII optimization algorithms.

    Techniques Used:



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    codes of implementation of lssvm  (MathWorks Inc)
    90
    MathWorks Inc codes of implementation of lssvm
    Flowchart of N-fold cross-validation method. Stage I: Dividing dataset into two sets: modeling (80 %) and test (20 %). Stage II: Calculation of ‘ c ’ and <t>‘</t> <t>σ</t> ’ coefficients via GA or simplex algorithms; II-I: Dividing dataset into two sets of training and validation through cross-validation method; II-II: Training of <t>LSSVM</t> per different ‘ c ’ and ‘ σ ’ coefficients; II-III: Test of trained LSSVM model and fitness function calculation ; II-IV: Iteration of stages II-I , II-II , and II-III for obtaining the best ‘ c ’ and ‘ σ ’ coefficients. Stage III: Training of LSSVM model using modeling dataset and calculation of modeling error. Stage IV: Test of LSSVM model using test dataset, and calculation of test error.
    Codes Of Implementation Of Lssvm, 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/codes+of+implementation+of+lssvm/pmc06371636-91-9-0?v=MathWorks+Inc
    Average 90 stars, based on 1 article reviews
    codes of implementation of lssvm - by Bioz Stars, 2026-07
    90/100 stars
    		  Buy from Supplier

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    Flowchart of N-fold cross-validation method. Stage I: Dividing dataset into two sets: modeling (80 %) and test (20 %). Stage II: Calculation of ‘ c ’ and ‘ σ ’ coefficients via GA or simplex algorithms; II-I: Dividing dataset into two sets of training and validation through cross-validation method; II-II: Training of LSSVM per different ‘ c ’ and ‘ σ ’ coefficients; II-III: Test of trained LSSVM model and fitness function calculation ; II-IV: Iteration of stages II-I , II-II , and II-III for obtaining the best ‘ c ’ and ‘ σ ’ coefficients. Stage III: Training of LSSVM model using modeling dataset and calculation of modeling error. Stage IV: Test of LSSVM model using test dataset, and calculation of test error.

    Journal: Iranian Journal of Biotechnology

    Article Title: A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling

    doi: 10.21859/ijb.1542

    Figure Lengend Snippet: Flowchart of N-fold cross-validation method. Stage I: Dividing dataset into two sets: modeling (80 %) and test (20 %). Stage II: Calculation of ‘ c ’ and ‘ σ ’ coefficients via GA or simplex algorithms; II-I: Dividing dataset into two sets of training and validation through cross-validation method; II-II: Training of LSSVM per different ‘ c ’ and ‘ σ ’ coefficients; II-III: Test of trained LSSVM model and fitness function calculation ; II-IV: Iteration of stages II-I , II-II , and II-III for obtaining the best ‘ c ’ and ‘ σ ’ coefficients. Stage III: Training of LSSVM model using modeling dataset and calculation of modeling error. Stage IV: Test of LSSVM model using test dataset, and calculation of test error.

    Article Snippet: MATLAB (version 8.5.0.197613 - R2015a) codes of implementation of LSSVM using the optimized ‘ c ’ and ‘ σ ’ coefficients obtained from NSGA-II, for E. coli growth dataset are presented as Supplementary Note 2 Click here for additional data file.

    Techniques: Biomarker Discovery

    LSSVM parameters over Simplex, GA and NSGAII optimization algorithms.

    Journal: Iranian Journal of Biotechnology

    Article Title: A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling

    doi: 10.21859/ijb.1542

    Figure Lengend Snippet: LSSVM parameters over Simplex, GA and NSGAII optimization algorithms.

    Article Snippet: MATLAB (version 8.5.0.197613 - R2015a) codes of implementation of LSSVM using the optimized ‘ c ’ and ‘ σ ’ coefficients obtained from NSGA-II, for E. coli growth dataset are presented as Supplementary Note 2 Click here for additional data file.

    Techniques: