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

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

Short Time Series of Website Visits Prediction by RBF Neural Networks and Support Vector Machine Regression

Artificial Intelligence and Soft Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-29347-4_16 ◽

2012 ◽

pp. 135-142 ◽

Cited By ~ 1

Author(s):

Vladimir Olej ◽

Jana Filipova

Keyword(s):

Neural Networks ◽

Time Series ◽

Support Vector Machine ◽

Support Vector ◽

Rbf Neural Networks ◽

Support Vector Machine Regression ◽

Short Time Series ◽

Short Time

Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227ea) via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks

Applied Sciences ◽

10.3390/app6010025 ◽

2016 ◽

Vol 6 (1) ◽

pp. 25 ◽

Cited By ~ 18

Author(s):

Hao Li ◽

Xindong Tang ◽

Run Wang ◽

Fan Lin ◽

Zhijian Liu ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Comparative Study ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods ◽

Compressed Liquid ◽

Artificial Neural

A comparative study of support vector machine and neural networks for file type identification using n-gram analysis

Forensic Science International: Digital Investigation ◽

10.1016/j.fsidi.2021.301121 ◽

2021 ◽

Vol 36 ◽

pp. 301121

Author(s):

Joachim Sester ◽

Darren Hayes ◽

Mark Scanlon ◽

Nhien-An Le-Khac

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Comparative Study ◽

Support Vector ◽

N Gram

Comparative Study of Support Vector Machines and Neural Networks Used in Pattern Recognition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.562-564.2026 ◽

2012 ◽

Vol 562-564 ◽

pp. 2026-2029

Author(s):

Shu Xian Zhu ◽

Xue Li Zhu ◽

Sheng Hui Guo

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Pattern Recognition ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Support Vector Machines ◽

Theoretical Analysis ◽

Comparative Study ◽

Support Vector ◽

Vector Machines

Artificial neural networks and support vector machine (SVM), as two important tools, have widely applied in artificial intelligence and pattern recognition. In this paper, a comparative study has been done for making an analysis on their performances, when they are used in pattern recognition. Through theoretical analysis and confirmed by experimental results, a conclusion can be drawn that support vector machines have obvious advantages over those of traditional neural networks.

RBF Neural Networks Optimization Algorithm Based on Support Vector Machine and Its Application

2010 2nd International Conference on Information Engineering and Computer Science ◽

10.1109/iciecs.2010.5678389 ◽

2010 ◽

Author(s):

Jinxia Ren ◽

Sai Yang

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Optimization Algorithm ◽

Support Vector ◽

Rbf Neural Networks ◽

Neural Networks Optimization

Recognition of Color Objects Using Hybrids Descriptors

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2013100105 ◽

2013 ◽

Vol 3 (4) ◽

pp. 60-68

Author(s):

Driss Naji ◽

M. Fakir ◽

B. Bouikhalene ◽

M. Boutaounte

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Object Recognition ◽

Comparative Study ◽

Large Data ◽

Zernike Moment ◽

Support Vector ◽

Two Dimensional ◽

Large Data Base ◽

3D Objects

In this paper, the authors came up with a different approach based on the combination of the different descriptors. For object recognition, regardless of orientation, size and position, feature vectors are computed with the help of Zernike moments and Centrist descriptors. For a large data base the fact of using the classic descriptors has never been a satisfying method for perfect recognition rates. The authors deduced that the combination of descriptors can have good recognition rates, accordingthe result of a comparative study of the different descriptors and the different combinations (Zernike + Centrist, Zernike + ACP, Centrist + ACP). The Zernike moment with Centrist descriptors ended up being the best hybrid description. For the recognition process, the authors opted for support vector machine (SVM) and Neural Networks (NN). The authors illustrate the proposed method on 3D objects using representations of two-dimensional images that are taken from different angles of view are the main features leading the authors to their objective.

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

Sensor Dynamic Modeling Based on LS-SVM and NGA

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.381-382.439 ◽

2008 ◽

Vol 381-382 ◽

pp. 439-442

Author(s):

Qi Wang ◽

Zhi Gang Feng ◽

K. Shida

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Support Vector Machine ◽

Least Squares ◽

Dynamic Model ◽

Dynamic Modeling ◽

Support Vector ◽

Local Minima ◽

Highly Nonlinear ◽

Sharing Function

Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.

QSAR Study on the Toxicity of Phenols for Fathead Minnows by Using Support Vector Machine and Neural Networks

2008 Fourth International Conference on Natural Computation ◽

10.1109/icnc.2008.931 ◽

2008 ◽

Cited By ~ 1

Author(s):

Xiujun Cui ◽

Zhinxin Wang ◽

Zhuoyong Zhang ◽

Xing Yuan ◽

Peter de B. Harrington

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Support Vector ◽

Fathead Minnows ◽

Qsar Study