A comparison between linear-kernel SVM and RBF-kernel SVM.

Comparison of RKHS regression and RankSVM when using the RBF kernel with parameter γ=14pσ2 and when varying the regularization parameter λ=Nλ˜, where N = n (RKHS regression) or N = ∣P∣ (RankSVM).

Mathieu Blondel (754320)
Akio Onogi (3628712)
Hiroyoshi Iwata (487190)
Naonori Ueda (754321)

June 2015

The scores indicated are the test Mean NDCG@10 averaged over 10 CV iterations and across all trai...

Presenting Author’s Biography

D. Boolch

August 2015

Support Vector Machines (SVM) have been used for creating fast and efficient models for quickly pred...

Comparison of the testing accuracy of H-ELM, ELM, SVM linear, and SVM RBF in a multiclass setting using conventional validation and 10-fold cross-validation with RFE-SVM.

Muhammad Naveed Iqbal Qureshi (2921655)
Beomjun Min (2921661)
Hang Joon Jo (122179)
Boreom Lee (2921658)

August 2016

RCD represents the ranked classification dataset size acquired by cumulative RFE-SVM to ac...

Best results by SVM on WT and WP datasets.

Stephen Gang Wu (3173373)
Yuxuan Wang (251760)
Wu Jiang (189621)
Tolutola Oyetunde (3173367)
Ruilian Yao (3173370)
Xuehong Zhang (194268)
Kazuyuki Shimizu (813934)
Yinjie J. Tang (185522)
Forrest Sheng Bao (266071)

April 2016

Grid searches are performed on both linear and RBF kernels. The results from WP dataset are much ...

An empirical assessment of different kernel functions on the performance of support vector machines

Isaac Kofi Nti
Owusu Nyarko-Boateng
Felix Adebayo Adekoya
Benjamin Asubam Weyori

January 2022

Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day ...

SVM based performance on testing dataset (5-fold cross-validation) using parameters t = 2 (RBF kernel), and g = 1, c = 0.1, j = 2.

Ranjan Kumar Barman (619513)
Sudipto Saha (64064)
Santasabuj Das (619514)

November 2014

SVM based performance on testing dataset (5-fold cross-validation) using parameters t = 2 (RBF ke...

Comparison of kernelPLS with four other methods. For 10-fold cross validation classification accuracy(%) and AUC (%) of SVM on two-class datasets.

Shiquan Sun (601903)
Qinke Peng (601904)
Adnan Shakoor (601905)

July 2014

Comparison of kernelPLS with four other methods. For 10-fold cross validation classification accu...

Towards Understanding the Influence of SVM

Charl J. Van Heerden
Etienne Barnard

September 2016

Abstract—We investigate the relationship between SVM hy-perparameters for linear and RBF kernels and...

Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification

E.A. Zanaty

November 2012

In this paper, we introduce a new kernel function for improving the accuracy of the Support Vector M...

Performance of SVM model for various combinations of genes tested. RBF kernel function (γ value = 1.0 and C value = 10) was utilized for model building.

Ana Lisa V. Gomes (363686)
Lawrence J. K. Wee (363687)
Asif M. Khan (36339)
Laura H. V. G. Gil (257230)
Ernesto T. A. Marques Jr (200766)
Carlos E. Calzavara-Silva (257213)
Tin Wee Tan (5366)

February 2013

Performance of SVM model for various combinations of genes tested. RBF kernel function (γ value =...

Android mobile application for wildfire reporting and monitoring

Rony Teguh
Fengky F. Adji
Benius
Mohammad Nur Aulia

December 2021

Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day ...

Comparison of kernelPLS with four other methods. For 5-fold cross validation classification accuracy(%) and Cohen's kappa coefficient of SVM on multi-class datasets.

Shiquan Sun (601903)
Qinke Peng (601904)
Adnan Shakoor (601905)

July 2014

Comparison of kernelPLS with four other methods. For 5-fold cross validation classification accur...

Validate Model Endorsed for Support Vector Machine Alignment with Kernel Function and Depth Concept to Get Superlative Accurateness

Dr. R. Muthukrishnan
N. Udaya Prakash

March 2023

Abstract: A support vector machine (SVM) is authoritative tool for statistical learning model which ...

Explaining Support Vector Machines: A Color Based Nomogram

Van Belle Vanya
Van Calster Ben
Van Huffel Sabine
Suykens Johan A. K.
Lisboa Paulo

October 2016

Support vector machines (SVMs) are very popular tools for classification, regression and other probl...

Analysis of kernel matrices and their relation to SVM performance

Wiejacha, Malgorzata

The thesis explores the relationship between kernel matrix properties and RVM model performance in t...

Comparison of RKHS regression and RankSVM when using the RBF kernel with parameter γ=14pσ2 and when varying the regularization parameter λ=Nλ˜, where N = n (RKHS regression) or N = ∣P∣ (RankSVM).

Mathieu Blondel (754320)
Akio Onogi (3628712)
Hiroyoshi Iwata (487190)
Naonori Ueda (754321)

June 2015

The scores indicated are the test Mean NDCG@10 averaged over 10 CV iterations and across all trai...

Presenting Author’s Biography

D. Boolch

August 2015

Support Vector Machines (SVM) have been used for creating fast and efficient models for quickly pred...

Comparison of the testing accuracy of H-ELM, ELM, SVM linear, and SVM RBF in a multiclass setting using conventional validation and 10-fold cross-validation with RFE-SVM.

Muhammad Naveed Iqbal Qureshi (2921655)
Beomjun Min (2921661)
Hang Joon Jo (122179)
Boreom Lee (2921658)

August 2016

RCD represents the ranked classification dataset size acquired by cumulative RFE-SVM to ac...

Best results by SVM on WT and WP datasets.

Stephen Gang Wu (3173373)
Yuxuan Wang (251760)
Wu Jiang (189621)
Tolutola Oyetunde (3173367)
Ruilian Yao (3173370)
Xuehong Zhang (194268)
Kazuyuki Shimizu (813934)
Yinjie J. Tang (185522)
Forrest Sheng Bao (266071)

April 2016

Grid searches are performed on both linear and RBF kernels. The results from WP dataset are much ...

An empirical assessment of different kernel functions on the performance of support vector machines

Isaac Kofi Nti
Owusu Nyarko-Boateng
Felix Adebayo Adekoya
Benjamin Asubam Weyori

January 2022

Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day ...

SVM based performance on testing dataset (5-fold cross-validation) using parameters t = 2 (RBF kernel), and g = 1, c = 0.1, j = 2.

Ranjan Kumar Barman (619513)
Sudipto Saha (64064)
Santasabuj Das (619514)

November 2014

SVM based performance on testing dataset (5-fold cross-validation) using parameters t = 2 (RBF ke...

Comparison of kernelPLS with four other methods. For 10-fold cross validation classification accuracy(%) and AUC (%) of SVM on two-class datasets.

Shiquan Sun (601903)
Qinke Peng (601904)
Adnan Shakoor (601905)

July 2014

Comparison of kernelPLS with four other methods. For 10-fold cross validation classification accu...

Towards Understanding the Influence of SVM

Charl J. Van Heerden
Etienne Barnard

September 2016

Abstract—We investigate the relationship between SVM hy-perparameters for linear and RBF kernels and...

Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification

E.A. Zanaty

November 2012

In this paper, we introduce a new kernel function for improving the accuracy of the Support Vector M...

Performance of SVM model for various combinations of genes tested. RBF kernel function (γ value = 1.0 and C value = 10) was utilized for model building.

Ana Lisa V. Gomes (363686)
Lawrence J. K. Wee (363687)
Asif M. Khan (36339)
Laura H. V. G. Gil (257230)
Ernesto T. A. Marques Jr (200766)
Carlos E. Calzavara-Silva (257213)
Tin Wee Tan (5366)

February 2013

Performance of SVM model for various combinations of genes tested. RBF kernel function (γ value =...

Android mobile application for wildfire reporting and monitoring

Rony Teguh
Fengky F. Adji
Benius
Mohammad Nur Aulia

December 2021

Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day ...

Comparison of kernelPLS with four other methods. For 5-fold cross validation classification accuracy(%) and Cohen's kappa coefficient of SVM on multi-class datasets.

Shiquan Sun (601903)
Qinke Peng (601904)
Adnan Shakoor (601905)

July 2014

Comparison of kernelPLS with four other methods. For 5-fold cross validation classification accur...

Validate Model Endorsed for Support Vector Machine Alignment with Kernel Function and Depth Concept to Get Superlative Accurateness

Dr. R. Muthukrishnan
N. Udaya Prakash

March 2023

Abstract: A support vector machine (SVM) is authoritative tool for statistical learning model which ...

Explaining Support Vector Machines: A Color Based Nomogram

Van Belle Vanya
Van Calster Ben
Van Huffel Sabine
Suykens Johan A. K.
Lisboa Paulo

October 2016

Support vector machines (SVMs) are very popular tools for classification, regression and other probl...

Analysis of kernel matrices and their relation to SVM performance

Wiejacha, Malgorzata

The thesis explores the relationship between kernel matrix properties and RVM model performance in t...

Comparison of RKHS regression and RankSVM when using the RBF kernel with parameter γ=14pσ2 and when varying the regularization parameter λ=Nλ˜, where N = n (RKHS regression) or N = ∣P∣ (RankSVM).

Mathieu Blondel (754320)
Akio Onogi (3628712)
Hiroyoshi Iwata (487190)
Naonori Ueda (754321)

June 2015

The scores indicated are the test Mean NDCG@10 averaged over 10 CV iterations and across all trai...

Presenting Author’s Biography

D. Boolch

August 2015

Support Vector Machines (SVM) have been used for creating fast and efficient models for quickly pred...

Comparison of the testing accuracy of H-ELM, ELM, SVM linear, and SVM RBF in a multiclass setting using conventional validation and 10-fold cross-validation with RFE-SVM.

Muhammad Naveed Iqbal Qureshi (2921655)
Beomjun Min (2921661)
Hang Joon Jo (122179)
Boreom Lee (2921658)

August 2016

RCD represents the ranked classification dataset size acquired by cumulative RFE-SVM to ac...

A comparison between linear-kernel SVM and RBF-kernel SVM.

Abstract

Extracted data

A comparison between linear-kernel SVM and RBF-kernel SVM.

Abstract

Extracted data

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