Combining Recurrent Neural Networks and Support Vector Machines for Structural Pattern Recognition

2004 ◽  
pp. 241-255
Author(s):  
Brijnesh J. Jain ◽  
Peter Geibel ◽  
Fritz Wysotzki
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Support Vector Machines ◽  
Recurrent Neural Networks ◽  
Support Vector ◽  
Structural Pattern ◽  
Structural Pattern Recognition ◽  
Vector Machines

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.

scholarly journals RECURRENT NEURAL NETWORKS AND NONLINEAR PREDICTION IN SUPPORT VECTOR MACHINES

2019 ◽  
Vol 2019 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Jennifer S. Raj ◽  
Vijitha Ananthi J
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Recurrent Neural Networks ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Support Vector ◽  
Nonlinear Prediction ◽  
Proposed Model ◽  
Vector Machines ◽  
Robust Prediction

The nonlinear regression estimation issues are solved by successful application of a novel neural network technique termed as support vector machines (SVMs). Evaluation of recurrent neural networks (RNNs) can assist in pattern recognition of several real-time applications and reduce the pattern mismatch. This paper provides a robust prediction model for multiple applications. Traditionally, back-propagation algorithms were used for training RNN. This paper predict system reliability by applying SVM learning algorithm to RNN. Comparison of the proposed model is done with the existing systems for analysis of prediction performance. These results indicate that the performance of proposed system exceeds that of the existing ones.

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.

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