Proceedings ArticleDOI
Unification of support vector machines and soft computing paradigms for pattern recognition
Ying Li,Licheng Jiao +1 more
- Vol. 4555, pp 154-159
Reads0
Chats0
TLDR
This paper analyzes support vector machines (SVMs) and several commonly used soft computing paradigms for pattern recognition including neural and wavelet networks, and fuzzy systems and tries to outline the similarities and differences among them.Abstract:
This paper analyzes support vector machines (SVMs) and several commonly used soft computing paradigms for pattern recognition including neural and wavelet networks, and fuzzy systems. Bayesian classifiers, fuzzy partitions, etc and tries to outline the similarities and differences among them. Support vector machines provide a new approach to the problem of pattern recognition with clear connections to the underlying statistical learning theory. We try to bring SVMs into the framework of the unification paradigm called the weighted radial basis function paradigm. Unifying different classes of methods has enormous advantages, such as the ability to merge all such techniques within the same system. It is hoped that this paper would provide theoretical guides for the study and applications of support vector machine and soft computing paradigms.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.read more
Citations
More filters
Journal ArticleDOI
Automatic defect classification in long-range ultrasonic rail inspection using a support vector machine-based ?smart system?
TL;DR: In this article, the authors presented the results from a pilot study of a "smart system" used for defect detection in railroad rails, particularly the critical transverse-type defects.
On-Line High-Speed Rail Defect Detection
TL;DR: In this paper, an ultrasonic method for defect detection in rails that substantially differs from conventional ultrasonic rail inspection is presented, in which the main difference is in the use of ultrasonic guided waves traveling along the rail running direction.
References
More filters
Journal ArticleDOI
Comparing support vector machines with Gaussian kernels to radial basis function classifiers
Bernhard Schölkopf,Kah-Kay Sung,C.J.C. Burges,Federico Girosi,Partha Niyogi,Tomaso Poggio,Vladimir Vapnik +6 more
TL;DR: The results show that on the United States postal service database of handwritten digits, the SV machine achieves the highest recognition accuracy, followed by the hybrid system, and the SV approach is thus not only theoretically well-founded but also superior in a practical application.
Journal ArticleDOI
Functional equivalence between radial basis function networks and fuzzy inference systems
TL;DR: It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent.
Journal ArticleDOI
Extending the functional equivalence of radial basis function networks and fuzzy inference systems
TL;DR: It is established the functional equivalence of a generalized class of Gaussian radial basis function networks and the full Takagi-Sugeno model (1983) of fuzzy inference and the more general framework allows the removal of some of the restrictive conditions.
Journal ArticleDOI
Unification of neural and wavelet networks and fuzzy systems
TL;DR: This paper analyzes several commonly used soft computing paradigms (neural and wavelet networks and fuzzy systems, Bayesian classifiers, fuzzy partitions, etc.) and tries to outline similarities and differences among each other.