Distribution of classifying connections.

Deborah Apthorp (377341)
D. Samuel Schwarzkopf (394157)
Christian Kaul (3293334)
Bahador Bahrami (376576)
David Alais (37900)
Geraint Rees (61610)

November 2016

Ranked accuracies from the support vector machine analysis described in the paper for all of the cla...

Comparison between the (i) degree distribution (number f of nodes with a given degree ki), (ii) assortativity (correlation between a node's degree ki and the mean degree of that node's neighbors , summarized by parameter r), and (iii) hierarchy (the relationship between the clustering coefficient Ci and the degree ki over all nodes in the network, summarized by parameter β) of the (A) Erdös-Rényi and (B) configuration model with conserved degree distribution models and the same diagnostics of the brain anatomical data (grey).

Florian Klimm (544145)
Danielle S. Bassett (215337)
Jean M. Carlson (215340)
Peter J. Mucha (463309)

March 2014

Black lines indicate best linear fit to the data (dashed) and model (solid) networks. In panel <i...

Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study

Sim, Doreen Ying Ying
Teh, Chee Siong
Ahmad Izuanuddin, Ismail

January 2020

Characteristics of Support Vector Machine (SVM) and its classifications are elaborated to show why i...

Classification results.

Martin M. Monti (473279)
Evan S. Lutkenhoff (302229)
Mikail Rubinov (218714)
Pierre Boveroux (447922)
Audrey Vanhaudenhuyse (162880)
Olivia Gosseries (447923)
Marie-Aurélie Bruno (335897)
Quentin Noirhomme (335898)
Mélanie Boly (69808)
Steven Laureys (62057)

October 2013

Top 1% connections contributing to the group SVM classification of (A) all wakefulness conditions...

Connectivity matrix for the six regions of interest from Xu & Chun (2006).

Alexander A. Stevens (187035)
Sarah C. Tappon (187040)
Arun Garg (187045)
Damien A. Fair (187049)

January 2012

The matrices indicate a) the intercorrelations among node-pairs for Session 1 (lower triangle) an...

Generation of connectivity networks in each group.

Cesar A. O. Coelho (5607731)
Tatiana L. Ferreira (5607734)
Juliana C. Kramer-Soares (5607737)
João R. Sato (153911)
Maria Gabriela M. Oliveira (5607740)

August 2018

After computing the inter-regional correlations (left), three thresholds were applied (p < 0.05, ...

Distribution and Reliability of SVM Scores

Jinfeng Liu (32678)
Julian Gough (19646)
Burkhard Rost (6137)

February 2013

<div>(A) The gray shading indicates prediction accuracy as a function of SVM score (left y...

Inter-relations of MAPPIN'SDM foci.

Jürgen Kasper (171888)
Frauke Hoffmann (134729)
Christoph Heesen (116779)
Sascha Köpke (171899)
Friedemann Geiger (171906)

April 2012

The table shows Pearson correlation coefficients of pairwise related judgements by MAPPIN'SDM dif...

Categorization of positive and negative human connections.

Gadi Goelman (661275)
Noam Gordon (661276)
Omer Bonne (661277)

November 2014

Percentages of significant connections into the ‘Intra Cx’ (between two cortical ROIs), the ‘Extr...

The correlation matrix for connection weights between nodes and VSTM.

Alexander A. Stevens (187035)
Sarah C. Tappon (187040)
Arun Garg (187045)
Damien A. Fair (187049)

January 2012

Capacity a) in Session 1 (lower triangle) and Session 2 (upper triangle). b) For cl...

Statistics for some representative examples of distributional semantic model (DSM) networks (n = 4,702).

Akira Utsumi (524739)

August 2015

Note.n = number of nodes; m = number of edges; nCC ...

Top 30% shared and distinct hubs and high-influence nodes in the RSN and SPN (normalized strength/degree range from 0.7 to 1.0).

Stefan Fuertinger (747489)
Barry Horwitz (340827)
Kristina Simonyan (659931)

July 2015

Left column: Shared high-strength and high-degree hubs in the RSN and SPN networks. Middle column...

This graph shows the distribution of top322 features.

Liqi Li (333968)
Xiang Cui (543707)
Sanjiu Yu (543708)
Yuan Zhang (41832)
Zhong Luo (543709)
Hua Yang (120668)
Yue Zhou (71752)
Xiaoqi Zheng (333971)

March 2014

SVM-REF ranked the features according to their ability to separate different categories for each ...

The Taxonomy Network correctly finds the empirically most active edges, as empirically calculated with the Assist Matrix, using two different approaches which are described in the text.

Andrea Zaccaria (671597)
Matthieu Cristelli (149874)
Andrea Tacchella (302213)
Luciano Pietronero (302211)

December 2014

Here we show the distribution of the rankings of the Assist Matrix elements selected by the Taxon...

Upper panel, frequency distributions of classification patterns identified by the SVM composite model (m1, m2, m3, m4) for the synthetic training dataset consisting of 4 subgroups, S1, S2, S3, and S4; Lower panel, performance of the SVM composite prediction model for the test dataset of 1,000 simulated samples.

Hung-Chia Chen (389376)
Wen Zou (389375)
Tzu-Pin Lu (145405)
James J. Chen (210865)

October 2014

Upper panel, frequency distributions of classification patterns identified by the SVM composite m...

Ranked SVM for all conditions

Deborah Apthorp (377341)
D. Samuel Schwarzkopf (394157)
Christian Kaul (3293334)
Bahador Bahrami (376576)
David Alais (37900)
Geraint Rees (61610)

November 2016

Ranked accuracies from the support vector machine analysis described in the paper for all of the cla...

Comparison between the (i) degree distribution (number f of nodes with a given degree ki), (ii) assortativity (correlation between a node's degree ki and the mean degree of that node's neighbors , summarized by parameter r), and (iii) hierarchy (the relationship between the clustering coefficient Ci and the degree ki over all nodes in the network, summarized by parameter β) of the (A) Erdös-Rényi and (B) configuration model with conserved degree distribution models and the same diagnostics of the brain anatomical data (grey).

Florian Klimm (544145)
Danielle S. Bassett (215337)
Jean M. Carlson (215340)
Peter J. Mucha (463309)

March 2014

Black lines indicate best linear fit to the data (dashed) and model (solid) networks. In panel <i...

Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study

Sim, Doreen Ying Ying
Teh, Chee Siong
Ahmad Izuanuddin, Ismail

January 2020

Characteristics of Support Vector Machine (SVM) and its classifications are elaborated to show why i...

Classification results.

Martin M. Monti (473279)
Evan S. Lutkenhoff (302229)
Mikail Rubinov (218714)
Pierre Boveroux (447922)
Audrey Vanhaudenhuyse (162880)
Olivia Gosseries (447923)
Marie-Aurélie Bruno (335897)
Quentin Noirhomme (335898)
Mélanie Boly (69808)
Steven Laureys (62057)

October 2013

Top 1% connections contributing to the group SVM classification of (A) all wakefulness conditions...

Connectivity matrix for the six regions of interest from Xu & Chun (2006).

Alexander A. Stevens (187035)
Sarah C. Tappon (187040)
Arun Garg (187045)
Damien A. Fair (187049)

January 2012

The matrices indicate a) the intercorrelations among node-pairs for Session 1 (lower triangle) an...

Generation of connectivity networks in each group.

Cesar A. O. Coelho (5607731)
Tatiana L. Ferreira (5607734)
Juliana C. Kramer-Soares (5607737)
João R. Sato (153911)
Maria Gabriela M. Oliveira (5607740)

August 2018

After computing the inter-regional correlations (left), three thresholds were applied (p < 0.05, ...

Distribution and Reliability of SVM Scores

Jinfeng Liu (32678)
Julian Gough (19646)
Burkhard Rost (6137)

February 2013

<div>(A) The gray shading indicates prediction accuracy as a function of SVM score (left y...

Inter-relations of MAPPIN'SDM foci.

Jürgen Kasper (171888)
Frauke Hoffmann (134729)
Christoph Heesen (116779)
Sascha Köpke (171899)
Friedemann Geiger (171906)

April 2012

The table shows Pearson correlation coefficients of pairwise related judgements by MAPPIN'SDM dif...

Categorization of positive and negative human connections.

Gadi Goelman (661275)
Noam Gordon (661276)
Omer Bonne (661277)

November 2014

Percentages of significant connections into the ‘Intra Cx’ (between two cortical ROIs), the ‘Extr...

The correlation matrix for connection weights between nodes and VSTM.

Alexander A. Stevens (187035)
Sarah C. Tappon (187040)
Arun Garg (187045)
Damien A. Fair (187049)

January 2012

Capacity a) in Session 1 (lower triangle) and Session 2 (upper triangle). b) For cl...

Statistics for some representative examples of distributional semantic model (DSM) networks (n = 4,702).

Akira Utsumi (524739)

August 2015

Note.n = number of nodes; m = number of edges; nCC ...

Top 30% shared and distinct hubs and high-influence nodes in the RSN and SPN (normalized strength/degree range from 0.7 to 1.0).

Stefan Fuertinger (747489)
Barry Horwitz (340827)
Kristina Simonyan (659931)

July 2015

Left column: Shared high-strength and high-degree hubs in the RSN and SPN networks. Middle column...

This graph shows the distribution of top322 features.

Liqi Li (333968)
Xiang Cui (543707)
Sanjiu Yu (543708)
Yuan Zhang (41832)
Zhong Luo (543709)
Hua Yang (120668)
Yue Zhou (71752)
Xiaoqi Zheng (333971)

March 2014

SVM-REF ranked the features according to their ability to separate different categories for each ...

The Taxonomy Network correctly finds the empirically most active edges, as empirically calculated with the Assist Matrix, using two different approaches which are described in the text.

Andrea Zaccaria (671597)
Matthieu Cristelli (149874)
Andrea Tacchella (302213)
Luciano Pietronero (302211)

December 2014

Here we show the distribution of the rankings of the Assist Matrix elements selected by the Taxon...

Upper panel, frequency distributions of classification patterns identified by the SVM composite model (m1, m2, m3, m4) for the synthetic training dataset consisting of 4 subgroups, S1, S2, S3, and S4; Lower panel, performance of the SVM composite prediction model for the test dataset of 1,000 simulated samples.

Hung-Chia Chen (389376)
Wen Zou (389375)
Tzu-Pin Lu (145405)
James J. Chen (210865)

October 2014

Upper panel, frequency distributions of classification patterns identified by the SVM composite m...

Ranked SVM for all conditions

Deborah Apthorp (377341)
D. Samuel Schwarzkopf (394157)
Christian Kaul (3293334)
Bahador Bahrami (376576)
David Alais (37900)
Geraint Rees (61610)

November 2016

Ranked accuracies from the support vector machine analysis described in the paper for all of the cla...

Comparison between the (i) degree distribution (number f of nodes with a given degree ki), (ii) assortativity (correlation between a node's degree ki and the mean degree of that node's neighbors , summarized by parameter r), and (iii) hierarchy (the relationship between the clustering coefficient Ci and the degree ki over all nodes in the network, summarized by parameter β) of the (A) Erdös-Rényi and (B) configuration model with conserved degree distribution models and the same diagnostics of the brain anatomical data (grey).

Florian Klimm (544145)
Danielle S. Bassett (215337)
Jean M. Carlson (215340)
Peter J. Mucha (463309)

March 2014

Black lines indicate best linear fit to the data (dashed) and model (solid) networks. In panel <i...

Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study

Sim, Doreen Ying Ying
Teh, Chee Siong
Ahmad Izuanuddin, Ismail

January 2020

Characteristics of Support Vector Machine (SVM) and its classifications are elaborated to show why i...

Distribution of classifying connections.

Abstract

Extracted data

Distribution of classifying connections.

Abstract

Extracted data

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