A comparative study of pathological voice based on vowels /a/ and /i/ using support vector machine

A convolutional neural network ‐based comparative study for pepper seed classification: Analysis of selected deep features with support vector machine

Journal of Food Process Engineering ◽

10.1111/jfpe.13955 ◽

2021 ◽

Author(s):

Kadir Sabanci ◽

Muhammet Fatih Aslan ◽

Ewa Ropelewska ◽

Muhammed Fahri Unlersen

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Comparative Study ◽

Convolutional Neural Network ◽

Support Vector ◽

Classification Analysis ◽

Pepper Seed

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Comparative Study on Crop Type Classification Using Support Vector Machine on UAV Imagery

Lecture Notes in Civil Engineering - Proceedings of UASG 2019 ◽

10.1007/978-3-030-37393-1_8 ◽

2020 ◽

pp. 67-77

Author(s):

Vijaya Kumar Vasantha ◽

Venkata Reddy Keesara

Keyword(s):

Support Vector Machine ◽

Comparative Study ◽

Support Vector ◽

Crop Type ◽

Type Classification

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A Comparative Study of Improved F-Score with Support Vector Machine and RBF Network for Breast Cancer Classification

International Journal of Machine Learning and Computing ◽

10.7763/ijmlc.2012.v2.227 ◽

2012 ◽

pp. 741-745 ◽

Cited By ~ 5

Author(s):

P. Jaganathan ◽

N. Rajkumar ◽

R. Kuppuchamy

Keyword(s):

Breast Cancer ◽

Support Vector Machine ◽

Comparative Study ◽

Cancer Classification ◽

Support Vector ◽

Rbf Network ◽

Breast Cancer Classification

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A Comparative Study and Performance Analysis of Classification Techniques: Support Vector Machine, Neural Networks and Decision Trees

Communications in Computer and Information Science - Advances in Computing and Data Sciences ◽

10.1007/978-981-10-5427-3_2 ◽

2017 ◽

pp. 13-21 ◽

Cited By ~ 1

Author(s):

Kumarshankar Raychaudhuri ◽

Manoj Kumar ◽

Sanjana Bhanu

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Performance Analysis ◽

Comparative Study ◽

Decision Trees ◽

Support Vector ◽

Classification Techniques ◽

And Performance

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Lane Change Intention Inference of Surrounding Vehicle: Comparative Study on Relevance Vector Machine (RVM) and Support Vector Machine (SVM)

10.23919/iccas52745.2021.9649734 ◽

2021 ◽

Author(s):

Jin Ho Yang ◽

Dae Jung Kim ◽

Chung Choo Chung

Keyword(s):

Support Vector Machine ◽

Comparative Study ◽

Relevance Vector Machine ◽

Support Vector ◽

Lane Change ◽

Intention Inference

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A Comparative Study of Supplier Selection Based on Support Vector Machine and RBF Neural Networks

International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012) Proceedings ◽

10.1007/978-3-642-38445-5_96 ◽

2013 ◽

pp. 917-926

Author(s):

Zao-jie Kong ◽

Jun-bin Xue

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Comparative Study ◽

Supplier Selection ◽

Support Vector ◽

Rbf Neural Networks

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A Comparative study between Support Vector Machine (SVM) and Extreme Learning Machine (ELM) for Fault Detection in Pumps

Indian Journal of Science and Technology ◽

10.17485/ijst/2016/v9i48/107915 ◽

2017 ◽

Vol 9 (48) ◽

Author(s):

V Muralidharan ◽

V Sugumaran

Keyword(s):

Support Vector Machine ◽

Fault Detection ◽

Comparative Study ◽

Extreme Learning Machine ◽

Support Vector ◽

Learning Machine

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Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

10.1063/1.5033289 ◽

2018 ◽

Author(s):

Niket Shastri ◽

Kamlesh Pathak

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Water Vapor ◽

Linear Regression ◽

Comparative Study ◽

Multiple Linear Regression ◽

Support Vector ◽

Network Support ◽

Artificial Neural

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A Comparative Study of Estimating Road Surface Condition Using Support Vector Machine and Deep Neural Networ

2019 IEEE Intelligent Transportation Systems Conference (ITSC) ◽

10.1109/itsc.2019.8916965 ◽

2019 ◽

Author(s):

Dae Jung Kim ◽

Jin Sung Kim ◽

Seung-Hi Lee ◽

Chung Choo Chung

Keyword(s):

Support Vector Machine ◽

Comparative Study ◽

Surface Condition ◽

Road Surface ◽

Support Vector

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Quantitative structure-toxicity relationships (QSTRs): A comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics toTetrahymena pyriformis§

SAR and QSAR in Environmental Research ◽

10.1080/10659360600562079 ◽

2006 ◽

Vol 17 (1) ◽

pp. 75-91 ◽

Cited By ~ 20

Author(s):

A. Panaye ◽

B. T. Fan ◽

J. P. Doucet ◽

X. J. Yao ◽

R. S. Zhang ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Comparative Study ◽

Radial Basis Function ◽

Basis Function ◽

General Regression Neural Network ◽

Support Vector ◽

Quantitative Structure ◽

Non Linear ◽

General Regression

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