ls-svm implementations Search Results


ls-svm toolbox27  (MathWorks Inc)
90
MathWorks Inc ls-svm toolbox27
Ls Svm Toolbox27, 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
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ls-svm implementations  (MathWorks Inc)
90
MathWorks Inc ls-svm implementations
Ls Svm Implementations, 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
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Average 90 stars, based on 1 article reviews
ls-svm implementations - by Bioz Stars, 2026-03
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ls-svm multiclass  (MathWorks Inc)
90
MathWorks Inc ls-svm multiclass
Ls Svm Multiclass, 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
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Average 90 stars, based on 1 article reviews
ls-svm multiclass - by Bioz Stars, 2026-03
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ls-svm toolbox  (MathWorks Inc)
90
MathWorks Inc ls-svm toolbox
Ls Svm Toolbox, 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/ls-svm toolbox/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
ls-svm toolbox - by Bioz Stars, 2026-03
90/100 stars
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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/result/codes of implementation of lssvm/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
codes of implementation of lssvm - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier

Image Search Results


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: