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FSS
2007
102views more  FSS 2007»
15 years 4 months ago
Extraction of fuzzy rules from support vector machines
The relationship between support vector machines (SVMs) and Takagi–Sugeno–Kang (TSK) fuzzy systems is shown. An exact representation of SVMs as TSK fuzzy systems is given for ...
Juan Luis Castro, L. D. Flores-Hidalgo, Carlos Jav...
ICNC
2005
Springer
15 years 10 months ago
Training Data Selection for Support Vector Machines
Abstract. In recent years, support vector machines (SVMs) have become a popular tool for pattern recognition and machine learning. Training a SVM involves solving a constrained qua...
Jigang Wang, Predrag Neskovic, Leon N. Cooper
BMCBI
2007
127views more  BMCBI 2007»
15 years 4 months ago
Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines
Background: Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occur approximately every 1000 bases in the overall human population. The no...
Jian Tian, Ningfeng Wu, Xuexia Guo, Jun Guo, Juhua...
FGR
2006
IEEE
297views Biometrics» more  FGR 2006»
15 years 10 months ago
Automatic Skin Segmentation for Gesture Recognition Combining Region and Support Vector Machine Active Learning
Skin segmentation is the cornerstone of many applications such as gesture recognition, face detection, and objectionable image filtering. In this paper, we attempt to address the ...
Junwei Han, George Awad, Alistair Sutherland, Hai ...
PKDD
2010
Springer
152views Data Mining» more  PKDD 2010»
15 years 3 months ago
Online Knowledge-Based Support Vector Machines
Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and the...
Gautam Kunapuli, Kristin P. Bennett, Amina Shabbee...