Computational results of the ICC algorithm for the illustrative example, where fsr, nsr and are calculated by averaging over 100 independent datasets with the standard deviation given in the parenthesis.

The average and scores produced by 50 consecutive runs of different algorithms for the Brain tumor data.

Anirban Mukhopadhyay (169006)
Sanghamitra Bandyopadhyay (169021)
Ujjwal Maulik (169015)

February 2013

The average and scores produced by 50 consecutive runs of different algorithms for the Brain tu...

Intraclass correlation coefficients (ICC), with their 95%CI and SEM (N/mm) for within- and between-day reliability analyses.

Isabelle Pagé (6069173)
François Nougarou (787677)
Arnaud Lardon (6069176)
Martin Descarreaux (81878)

December 2018

Intraclass correlation coefficients (ICC), with their 95%CI and SEM (N/mm) for within- and between-d...

Recognition performance (mean accuracy ± standard deviation) of the VLADs with the k-means and ours (hard objective) codebooks on the Butterflies, corresponding to the Fig 6.

Yuki Shinomiya (6194729)
Yukinobu Hoshino (6194732)

January 2019

Recognition performance (mean accuracy ± standard deviation) of the VLADs with the k-means and ours ...

Comparison of the ICC and regularization methods, where fsr, nsr and are calculated by averaging over 10 independent datasets with the standard deviation given in the parenthesis.

Qianyun Li (528993)
Runmin Shi (6407282)
Faming Liang (186676)

February 2019

The regularization methods were implemented with the Lasso, SCAD and MCP penalties.</p

The average and scores produced by 50 consecutive runs of different algorithms for the SRBCT data.

Anirban Mukhopadhyay (169006)
Sanghamitra Bandyopadhyay (169021)
Ujjwal Maulik (169015)

February 2013

The average and scores produced by 50 consecutive runs of different algorithms for the SRBCT da...

Prediction results of different methods for the illustrative example, where corr(Ytest,) and RMSE() are calculated by averaging over 100 independent datasets with the standard deviation given in the parenthesis.

Qianyun Li (528993)
Runmin Shi (6407282)
Faming Liang (186676)

February 2019

Prediction results of different methods for the illustrative example, where corr(Ytest,) and RMSE() ...

Computational methods in econometrics

Hajivassiliou, Vassilis

January 2018

The computational properties of an econometric method are fundamental determinants of its importance...

Performance of Algorithm 1.

David Lipshutz (7491059)
Aneesh Kashalikar (14573738)
Shiva Farashahi (5018381)
Dmitri B. Chklovskii (14573741)

February 2023

Accuracy of Algorithm 1 on the synthetic datasets (left) and the KC dataset (right). Each line denot...

Averages and standard deviations of computation time for datasets SRR407548, SRS011098, and ERR315856.

Shuji Suzuki (323794)
Masanori Kakuta (609780)
Takashi Ishida (69157)
Yutaka Akiyama (323798)

August 2016

Averages and standard deviations of computation time for datasets SRR407548, SRS011098, and ERR31...

Coefficient of correlation (r), coefficient of variation (CV) and standard deviation (SD) in the Ct values of each candidate gene calculated by the Bestkeeper algorithm for n = 12 and n = 33.

Kanta Chechi (326588)
Yves Gelinas (326589)
Patrick Mathieu (34911)
Yves Deshaies (326590)
Denis Richard (303777)

February 2013

Coefficient of correlation (r), coefficient of variation (CV) and standard deviation (SD) in the ...

The average metric values of different algorithms (Mean value (95%CI)).

Jingya Zhang (817471)
Jiajun Wang (512021)
Xiuying Wang (664529)
Xin Gao (14001)
Dagan Feng (817472)

October 2015

CI represents confidence interval.The average metric values of different algorithms (Mean ...

The average recognition rates (%) and standard deviations (%) of different algorithms on LFW database.

Chao Bi (437614)
Lei Zhang (38117)
Miao Qi (665154)
Caixia Zheng (3152838)
Yugen Yi (665152)
Jianzhong Wang (446991)
Baoxue Zhang (665155)

July 2016

The average recognition rates (%) and standard deviations (%) of different algorithms on LFW data...

Efficacy of the dynamic algorithm evaluated by the global glycemic variation expressed by Index Correlation Class (ICC) and the percentage of coefficient variability for glucose (%CV).

Nathanaëlle Montanier (6269996)
Lise Bernard (3551984)
Céline Lambert (3180075)
Bruno Pereira (415597)
Françoise Desbiez (6269999)
Daniel Terral (3487457)
Armand Abergel (4966900)
Jérôme Bohatier (6270002)
Eugenio Rosset (6270005)
Jeannot Schmidt (432899)
Valérie Sautou (784405)
Samy Hadjadj (91107)
Marie Batisse-Lignier (140464)
Igor Tauveron (5047925)
Salwan Maqdasy (5047919)
Béatrice Roche (742932)

January 2019

Efficacy of the dynamic algorithm evaluated by the global glycemic variation expressed by Index Corr...

For the simulated datasets developed from 2-SNP models, average fraction over all 100 datasets of the total number of patterns checked before finding the true pattern.

Xia Jiang (210366)
Richard E. Neapolitan (302911)

February 2013

For the simulated datasets developed from 2-SNP models, average fraction over all 100 datasets of...

For the simulated datasets developed from 3-SNP models, average fraction over all 100 datasets of the total number of patterns checked before finding the true pattern.

Xia Jiang (210366)
Richard E. Neapolitan (302911)

February 2013

For the simulated datasets developed from 3-SNP models, average fraction over all 100 datasets of...

The average and scores produced by 50 consecutive runs of different algorithms for the Brain tumor data.

Anirban Mukhopadhyay (169006)
Sanghamitra Bandyopadhyay (169021)
Ujjwal Maulik (169015)

February 2013

The average and scores produced by 50 consecutive runs of different algorithms for the Brain tu...

Intraclass correlation coefficients (ICC), with their 95%CI and SEM (N/mm) for within- and between-day reliability analyses.

Isabelle Pagé (6069173)
François Nougarou (787677)
Arnaud Lardon (6069176)
Martin Descarreaux (81878)

December 2018

Intraclass correlation coefficients (ICC), with their 95%CI and SEM (N/mm) for within- and between-d...

Recognition performance (mean accuracy ± standard deviation) of the VLADs with the k-means and ours (hard objective) codebooks on the Butterflies, corresponding to the Fig 6.

Yuki Shinomiya (6194729)
Yukinobu Hoshino (6194732)

January 2019

Recognition performance (mean accuracy ± standard deviation) of the VLADs with the k-means and ours ...

Comparison of the ICC and regularization methods, where fsr, nsr and are calculated by averaging over 10 independent datasets with the standard deviation given in the parenthesis.

Qianyun Li (528993)
Runmin Shi (6407282)
Faming Liang (186676)

February 2019

The regularization methods were implemented with the Lasso, SCAD and MCP penalties.</p

The average and scores produced by 50 consecutive runs of different algorithms for the SRBCT data.

Anirban Mukhopadhyay (169006)
Sanghamitra Bandyopadhyay (169021)
Ujjwal Maulik (169015)

February 2013

The average and scores produced by 50 consecutive runs of different algorithms for the SRBCT da...

Prediction results of different methods for the illustrative example, where corr(Ytest,) and RMSE() are calculated by averaging over 100 independent datasets with the standard deviation given in the parenthesis.

Qianyun Li (528993)
Runmin Shi (6407282)
Faming Liang (186676)

February 2019

Prediction results of different methods for the illustrative example, where corr(Ytest,) and RMSE() ...

Computational methods in econometrics

Hajivassiliou, Vassilis

January 2018

The computational properties of an econometric method are fundamental determinants of its importance...

Performance of Algorithm 1.

David Lipshutz (7491059)
Aneesh Kashalikar (14573738)
Shiva Farashahi (5018381)
Dmitri B. Chklovskii (14573741)

February 2023

Accuracy of Algorithm 1 on the synthetic datasets (left) and the KC dataset (right). Each line denot...

Averages and standard deviations of computation time for datasets SRR407548, SRS011098, and ERR315856.

Shuji Suzuki (323794)
Masanori Kakuta (609780)
Takashi Ishida (69157)
Yutaka Akiyama (323798)

August 2016

Averages and standard deviations of computation time for datasets SRR407548, SRS011098, and ERR31...

Coefficient of correlation (r), coefficient of variation (CV) and standard deviation (SD) in the Ct values of each candidate gene calculated by the Bestkeeper algorithm for n = 12 and n = 33.

Kanta Chechi (326588)
Yves Gelinas (326589)
Patrick Mathieu (34911)
Yves Deshaies (326590)
Denis Richard (303777)

February 2013

Coefficient of correlation (r), coefficient of variation (CV) and standard deviation (SD) in the ...

The average metric values of different algorithms (Mean value (95%CI)).

Jingya Zhang (817471)
Jiajun Wang (512021)
Xiuying Wang (664529)
Xin Gao (14001)
Dagan Feng (817472)

October 2015

CI represents confidence interval.The average metric values of different algorithms (Mean ...

The average recognition rates (%) and standard deviations (%) of different algorithms on LFW database.

Chao Bi (437614)
Lei Zhang (38117)
Miao Qi (665154)
Caixia Zheng (3152838)
Yugen Yi (665152)
Jianzhong Wang (446991)
Baoxue Zhang (665155)

July 2016

The average recognition rates (%) and standard deviations (%) of different algorithms on LFW data...

Efficacy of the dynamic algorithm evaluated by the global glycemic variation expressed by Index Correlation Class (ICC) and the percentage of coefficient variability for glucose (%CV).

Nathanaëlle Montanier (6269996)
Lise Bernard (3551984)
Céline Lambert (3180075)
Bruno Pereira (415597)
Françoise Desbiez (6269999)
Daniel Terral (3487457)
Armand Abergel (4966900)
Jérôme Bohatier (6270002)
Eugenio Rosset (6270005)
Jeannot Schmidt (432899)
Valérie Sautou (784405)
Samy Hadjadj (91107)
Marie Batisse-Lignier (140464)
Igor Tauveron (5047925)
Salwan Maqdasy (5047919)
Béatrice Roche (742932)

January 2019

Efficacy of the dynamic algorithm evaluated by the global glycemic variation expressed by Index Corr...

For the simulated datasets developed from 2-SNP models, average fraction over all 100 datasets of the total number of patterns checked before finding the true pattern.

Xia Jiang (210366)
Richard E. Neapolitan (302911)

February 2013

For the simulated datasets developed from 2-SNP models, average fraction over all 100 datasets of...

For the simulated datasets developed from 3-SNP models, average fraction over all 100 datasets of the total number of patterns checked before finding the true pattern.

Xia Jiang (210366)
Richard E. Neapolitan (302911)

February 2013

For the simulated datasets developed from 3-SNP models, average fraction over all 100 datasets of...

The average and scores produced by 50 consecutive runs of different algorithms for the Brain tumor data.

Anirban Mukhopadhyay (169006)
Sanghamitra Bandyopadhyay (169021)
Ujjwal Maulik (169015)

February 2013

The average and scores produced by 50 consecutive runs of different algorithms for the Brain tu...

Intraclass correlation coefficients (ICC), with their 95%CI and SEM (N/mm) for within- and between-day reliability analyses.

Isabelle Pagé (6069173)
François Nougarou (787677)
Arnaud Lardon (6069176)
Martin Descarreaux (81878)

December 2018

Intraclass correlation coefficients (ICC), with their 95%CI and SEM (N/mm) for within- and between-d...

Recognition performance (mean accuracy ± standard deviation) of the VLADs with the k-means and ours (hard objective) codebooks on the Butterflies, corresponding to the Fig 6.

Yuki Shinomiya (6194729)
Yukinobu Hoshino (6194732)

January 2019

Recognition performance (mean accuracy ± standard deviation) of the VLADs with the k-means and ours ...

Computational results of the ICC algorithm for the illustrative example, where fsr, nsr and are calculated by averaging over 100 independent datasets with the standard deviation given in the parenthesis.

Abstract

Extracted data

Computational results of the ICC algorithm for the illustrative example, where fsr, nsr and are calculated by averaging over 100 independent datasets with the standard deviation given in the parenthesis.

Abstract

Extracted data

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