Single-trial subject dependent and subject independent SVM classifier performance on identical input features.

Comparison of independent testing performance between single SVM and MDDLogo-clustered SVM models.

Yi-Ju Chen (213642)
Cheng-Tsung Lu (149125)
Kai-Yao Huang (149129)
Hsin-Yi Wu (718313)
Yu-Ju Chen (213651)
Tzong-Yi Lee (10134)

April 2015

Sn, sensitivity; Sp, specificity; Acc, accuracy; MCC, Matthews Correlation Coefficient.</p

Accuracy of each SVM classifiers in a within- and across-experimental condition evaluation.

Emiliano Ricciardi (385660)
Giacomo Handjaras (385661)
Daniela Bonino (385662)
Tomaso Vecchi (385663)
Luciano Fadiga (41646)
Pietro Pietrini (385664)

March 2013

***p-value < 0.005,**p-value < 0.01,*p-value < 0.05 at permutation test.</p

Performance of sparse and non-sparse discriminant models in internal validation compared on the same dataset.

Osamu Matsuda (756159)
Yoshinori Ohara (17014574)

September 2023

Performance of sparse and non-sparse discriminant models in internal validation compared on the same...

Single-trial subject dependent and subject independent AdaBoost classifier performance on identical input features.

Dhanya Menoth Mohan (849848)
Parmod Kumar (2563381)
Faisal Mahmood (815679)
Kian Foong Wong (849849)
Abhishek Agrawal (849850)
Mohamed Elgendi (459412)
Rohit Shukla (849851)
Natania Ang (849852)
April Ching (849853)
Justin Dauwels (677854)
Alice H. D. Chan (172767)

February 2016

Single-trial subject dependent and subject independent AdaBoost classifier performance on identic...

Performance of the subject-dependent classifiers for single-trial ERP data.

Dhanya Menoth Mohan (849848)
Parmod Kumar (2563381)
Faisal Mahmood (815679)
Kian Foong Wong (849849)
Abhishek Agrawal (849850)
Mohamed Elgendi (459412)
Rohit Shukla (849851)
Natania Ang (849852)
April Ching (849853)
Justin Dauwels (677854)
Alice H. D. Chan (172767)

February 2016

Performance of the subject-dependent classifiers for single-trial ERP data.</p

Performance comparison of stand-alone SVM model.

Sait Can Yücebaş (540471)
Yeşim Aydın Son (540472)

March 2014

SVM model is tested by three different datasets, only genotype, only phenotype and integrated phe...

Single feature class performance.

Clemens Wrzodek (324317)
Finja Büchel (324322)
Georg Hinselmann (324325)
Johannes Eichner (236560)
Florian Mittag (324327)
Andreas Zell (81517)

February 2013

Comparison of predictive performances of single feature classes. All values are taken from SVM pr...

Binary SVM and AdaBoost classifier performance for average-ERP data using identical input features.

Dhanya Menoth Mohan (849848)
Parmod Kumar (2563381)
Faisal Mahmood (815679)
Kian Foong Wong (849849)
Abhishek Agrawal (849850)
Mohamed Elgendi (459412)
Rohit Shukla (849851)
Natania Ang (849852)
April Ching (849853)
Justin Dauwels (677854)
Alice H. D. Chan (172767)

February 2016

Binary SVM and AdaBoost classifier performance for average-ERP data using identical input feature...

Single-trial ERP classification results (SVM (left) and AdaBoost (right)) of individual subjects (subject#1 to subject#40).

Dhanya Menoth Mohan (849848)
Parmod Kumar (2563381)
Faisal Mahmood (815679)
Kian Foong Wong (849849)
Abhishek Agrawal (849850)
Mohamed Elgendi (459412)
Rohit Shukla (849851)
Natania Ang (849852)
April Ching (849853)
Justin Dauwels (677854)
Alice H. D. Chan (172767)

February 2016

Single-trial ERP classification results (SVM (left) and AdaBoost (right)) of individual subjects ...

LSTM features used in an SVM classifier.

Artzai Picon (6714263)
Unai Irusta (3149007)
Aitor Álvarez-Gila (6714266)
Elisabete Aramendi (3149013)
Felipe Alonso-Atienza (3149019)
Carlos Figuera (3149004)
Unai Ayala (3149022)
Estibaliz Garrote (6714269)
Lars Wik (3149010)
Jo Kramer-Johansen (3149001)
Trygve Eftestøl (3148998)

May 2019

Performance of an SVM classifier based on LSTM features, compared to the best baseline classifier us...

Performance comparison of SVM classifiers with different kernel functions and parameters.

Huilin Wang (620172)
Mingjun Wang (141725)
Hao Tan (117484)
Yuan Li (67017)
Ziding Zhang (145526)
Jiangning Song (59322)

August 2014

Performance was evaluated based on the AUC scores using independent tests.</p

The Performance of SVM Models on test dataset D5.

Mohit Mazumder (172623)
Narendra Padhan (554942)
Alok Bhattacharya (56260)
Samudrala Gourinath (31302)

April 2014

The Performance of SVM Models on test dataset D5 (experimental binding affinities obtained from l...

Performance results across classifiers.

Barry McDermott (5509082)
Martin O’Halloran (5509079)
Emily Porter (3556388)
Adam Santorelli (5509085)

July 2018

The sensitivity, specificity and accuracy of each of three classifiers (Linear SVM, RBF SVM, NN) ...

Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cross validation.

Lina Zhang (112887)
Chengjin Zhang (548663)
Rui Gao (247606)
Runtao Yang (697712)
Qing Song (77132)

September 2016

Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...

Individual feature selection in each One-versus-One classifier improves multi-class SVM performance

Phoenix X. Huang
Robert B. Fisher

December 2014

Abstract—Multiclass One-versus-One (OvO) SVM, which is constructed by assembling a group of binary c...

Comparison of independent testing performance between single SVM and MDDLogo-clustered SVM models.

Yi-Ju Chen (213642)
Cheng-Tsung Lu (149125)
Kai-Yao Huang (149129)
Hsin-Yi Wu (718313)
Yu-Ju Chen (213651)
Tzong-Yi Lee (10134)

April 2015

Sn, sensitivity; Sp, specificity; Acc, accuracy; MCC, Matthews Correlation Coefficient.</p

Accuracy of each SVM classifiers in a within- and across-experimental condition evaluation.

Emiliano Ricciardi (385660)
Giacomo Handjaras (385661)
Daniela Bonino (385662)
Tomaso Vecchi (385663)
Luciano Fadiga (41646)
Pietro Pietrini (385664)

March 2013

***p-value < 0.005,**p-value < 0.01,*p-value < 0.05 at permutation test.</p

Performance of sparse and non-sparse discriminant models in internal validation compared on the same dataset.

Osamu Matsuda (756159)
Yoshinori Ohara (17014574)

September 2023

Performance of sparse and non-sparse discriminant models in internal validation compared on the same...

Single-trial subject dependent and subject independent AdaBoost classifier performance on identical input features.

Dhanya Menoth Mohan (849848)
Parmod Kumar (2563381)
Faisal Mahmood (815679)
Kian Foong Wong (849849)
Abhishek Agrawal (849850)
Mohamed Elgendi (459412)
Rohit Shukla (849851)
Natania Ang (849852)
April Ching (849853)
Justin Dauwels (677854)
Alice H. D. Chan (172767)

February 2016

Single-trial subject dependent and subject independent AdaBoost classifier performance on identic...

Performance of the subject-dependent classifiers for single-trial ERP data.

Dhanya Menoth Mohan (849848)
Parmod Kumar (2563381)
Faisal Mahmood (815679)
Kian Foong Wong (849849)
Abhishek Agrawal (849850)
Mohamed Elgendi (459412)
Rohit Shukla (849851)
Natania Ang (849852)
April Ching (849853)
Justin Dauwels (677854)
Alice H. D. Chan (172767)

February 2016

Performance of the subject-dependent classifiers for single-trial ERP data.</p

Performance comparison of stand-alone SVM model.

Sait Can Yücebaş (540471)
Yeşim Aydın Son (540472)

March 2014

SVM model is tested by three different datasets, only genotype, only phenotype and integrated phe...

Single feature class performance.

Clemens Wrzodek (324317)
Finja Büchel (324322)
Georg Hinselmann (324325)
Johannes Eichner (236560)
Florian Mittag (324327)
Andreas Zell (81517)

February 2013

Comparison of predictive performances of single feature classes. All values are taken from SVM pr...

Binary SVM and AdaBoost classifier performance for average-ERP data using identical input features.

Dhanya Menoth Mohan (849848)
Parmod Kumar (2563381)
Faisal Mahmood (815679)
Kian Foong Wong (849849)
Abhishek Agrawal (849850)
Mohamed Elgendi (459412)
Rohit Shukla (849851)
Natania Ang (849852)
April Ching (849853)
Justin Dauwels (677854)
Alice H. D. Chan (172767)

February 2016

Binary SVM and AdaBoost classifier performance for average-ERP data using identical input feature...

Single-trial ERP classification results (SVM (left) and AdaBoost (right)) of individual subjects (subject#1 to subject#40).

Dhanya Menoth Mohan (849848)
Parmod Kumar (2563381)
Faisal Mahmood (815679)
Kian Foong Wong (849849)
Abhishek Agrawal (849850)
Mohamed Elgendi (459412)
Rohit Shukla (849851)
Natania Ang (849852)
April Ching (849853)
Justin Dauwels (677854)
Alice H. D. Chan (172767)

February 2016

Single-trial ERP classification results (SVM (left) and AdaBoost (right)) of individual subjects ...

LSTM features used in an SVM classifier.

Artzai Picon (6714263)
Unai Irusta (3149007)
Aitor Álvarez-Gila (6714266)
Elisabete Aramendi (3149013)
Felipe Alonso-Atienza (3149019)
Carlos Figuera (3149004)
Unai Ayala (3149022)
Estibaliz Garrote (6714269)
Lars Wik (3149010)
Jo Kramer-Johansen (3149001)
Trygve Eftestøl (3148998)

May 2019

Performance of an SVM classifier based on LSTM features, compared to the best baseline classifier us...

Performance comparison of SVM classifiers with different kernel functions and parameters.

Huilin Wang (620172)
Mingjun Wang (141725)
Hao Tan (117484)
Yuan Li (67017)
Ziding Zhang (145526)
Jiangning Song (59322)

August 2014

Performance was evaluated based on the AUC scores using independent tests.</p

The Performance of SVM Models on test dataset D5.

Mohit Mazumder (172623)
Narendra Padhan (554942)
Alok Bhattacharya (56260)
Samudrala Gourinath (31302)

April 2014

The Performance of SVM Models on test dataset D5 (experimental binding affinities obtained from l...

Performance results across classifiers.

Barry McDermott (5509082)
Martin O’Halloran (5509079)
Emily Porter (3556388)
Adam Santorelli (5509085)

July 2018

The sensitivity, specificity and accuracy of each of three classifiers (Linear SVM, RBF SVM, NN) ...

Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cross validation.

Lina Zhang (112887)
Chengjin Zhang (548663)
Rui Gao (247606)
Runtao Yang (697712)
Qing Song (77132)

September 2016

Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...

Individual feature selection in each One-versus-One classifier improves multi-class SVM performance

Phoenix X. Huang
Robert B. Fisher

December 2014

Abstract—Multiclass One-versus-One (OvO) SVM, which is constructed by assembling a group of binary c...

Comparison of independent testing performance between single SVM and MDDLogo-clustered SVM models.

Yi-Ju Chen (213642)
Cheng-Tsung Lu (149125)
Kai-Yao Huang (149129)
Hsin-Yi Wu (718313)
Yu-Ju Chen (213651)
Tzong-Yi Lee (10134)

April 2015

Sn, sensitivity; Sp, specificity; Acc, accuracy; MCC, Matthews Correlation Coefficient.</p

Accuracy of each SVM classifiers in a within- and across-experimental condition evaluation.

Emiliano Ricciardi (385660)
Giacomo Handjaras (385661)
Daniela Bonino (385662)
Tomaso Vecchi (385663)
Luciano Fadiga (41646)
Pietro Pietrini (385664)

March 2013

***p-value < 0.005,**p-value < 0.01,*p-value < 0.05 at permutation test.</p

Performance of sparse and non-sparse discriminant models in internal validation compared on the same dataset.

Osamu Matsuda (756159)
Yoshinori Ohara (17014574)

September 2023

Performance of sparse and non-sparse discriminant models in internal validation compared on the same...

Single-trial subject dependent and subject independent SVM classifier performance on identical input features.

Abstract

Extracted data

Single-trial subject dependent and subject independent SVM classifier performance on identical input features.

Abstract

Extracted data

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