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

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.

scholarly journals Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Igor Peško ◽  
Vladimir Mučenski ◽  
Miloš Šešlija ◽  
Nebojša Radović ◽  
Aleksandra Vujkov ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Construction Projects ◽  
Percentage Error ◽  
Support Vector ◽  
Building Process ◽  
Vector Machines ◽  
Specific Part

Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE) of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.

A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran

2014 ◽  
Vol 119 (3-4) ◽  
pp. 723-731 ◽  
Author(s):  
Omid Hamidi ◽  
Jalal Poorolajal ◽  
Majid Sadeghifar ◽  
Hamed Abbasi ◽  
Zohreh Maryanaji ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Comparative Study ◽  
Support Vector ◽  
Vector Machines ◽  
Artificial Neural

Computational Intelligence Techniques

2011 ◽  
pp. 361-388
Author(s):  
Bharat Sundaram ◽  
Marimuthu Palaniswani ◽  
Alistair Shilton ◽  
Rezaul Begg
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Computational Intelligence ◽  
Human Movement ◽  
Support Vector ◽  
New Developments ◽  
Vector Machines

Computational intelligence (CI) encompasses approaches primarily based on artificial neural networks, fuzzy logic rules, evolutionary algorithms, support vector machines and also approaches that combine two or more techniques (hybrid). These methods have been applied to solve many complex and diverse problems. Recent years have seen many new developments in CI techniques and, consequently, this has led to many applications in a variety of areas including engineering, finance, social and biomedical. In particular, CI techniques are increasingly being used in biomedical and human movement areas because of the complexity of the biological systems. The main objective of this chapter is to provide a brief description of the major computational intelligence techniques for pattern recognition and modelling tasks that often appear in biomedical, health and human movement research.

Computational Intelligence Techniques

2011 ◽  
pp. 139-171
Author(s):  
Bharat Sundaram ◽  
Marimuthu Palaniswani ◽  
Alistair Shilton ◽  
Rezaul Begg
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Computational Intelligence ◽  
Human Movement ◽  
Support Vector ◽  
New Developments ◽  
Vector Machines

Computational intelligence (CI) encompasses approaches primarily based on artificial neural networks, fuzzy logic rules, evolutionary algorithms, support vector machines and also approaches that combine two or more techniques (hybrid). These methods have been applied to solve many complex and diverse problems. Recent years have seen many new developments in CI techniques and, consequently, this has led to many applications in a variety of areas including engineering, finance, social and biomedical. In particular, CI techniques are increasingly being used in biomedical and human movement areas because of the complexity of the biological systems. The main objective of this chapter is to provide a brief description of the major computational intelligence techniques for pattern recognition and modelling tasks that often appear in biomedical, health and human movement research.

Predicting the Success of Ensemble Algorithms in the Banking Sector

2019 ◽  
Vol 6 (4) ◽  
pp. 12-31
Author(s):  
Özge Hüsniye Namlı Dağ
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Decision Tree ◽  
Banking Sector ◽  
Hybrid Methods ◽  
Support Vector ◽  
Vector Machines

The banking sector, like other service sector, improves in accordance with the customer's needs. Therefore, to know the needs of customers and to predict customer behaviors are very important for competition in the banking sector. Data mining uncovers relationships and hidden patterns in large data sets. Classification algorithms, one of the applications of data mining, is used very effectively in decision making. In this study, the c4.5 algorithm, a decision trees algorithm widely used in classification problems, is used in an integrated way with the ensemble machine learning methods in order to increase the efficiency of the algorithms. Data obtained via direct marketing campaigns from Portugal Banks was used to classify whether customers have term deposit accounts or not. Artificial Neural Networks and Support Vector Machines as Traditional Artificial Intelligence Methods and Bagging-C4.5 and Boosted-C.45 as ensemble-decision tree hybrid methods were used in classification. Bagging-C4.5 as ensemble-decision tree algorithm achieved more powerful classification success than other used algorithms. The ensemble-decision tree hybrid methods give better results than artificial neural networks and support vector machines as traditional artificial intelligence methods for this study.

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