The nature of statistical learning theory
The nature of statistical learning theory
Bayes classification based on minimum bounding spheres
Neurocomputing
A Fuzzy support vector classifier based on Bayesian optimization
Fuzzy Optimization and Decision Making
Weighted Hyper-sphere SVM for Hypertext Classification
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Fault classifier of rotating machinery based on weighted support vector data description
Expert Systems with Applications: An International Journal
Fuzzy multi-class classifier based on support vector data description and improved PCM
Expert Systems with Applications: An International Journal
A new maximal-margin spherical-structured multi-class support vector machine
Applied Intelligence
A new multi-class support vector machine with multi-sphere in the feature space
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Support vector classifier based on fuzzy c-means and Mahalanobis distance
Journal of Intelligent Information Systems
A computer-aided driving posture prediction system based on driver comfort
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
A note on hyper ellipse method for classifying biological and medical data
Computers in Biology and Medicine
Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection
Expert Systems with Applications: An International Journal
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Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVM approach was originally developed for binary classification problems. For solving multi-class classification problem, there are some methods such as one-against-rest, one-against-one, all-together and so on. But the computing time of all these methods are too long to solve large scale problem. In this paper SVMs architectures for multi-class problems are discussed, in particular we provide a new algorithm called sphere-structured SVMs to solve the multi-class problem. We show the algorithm in detail and analyze its characteristics. Not only the number of convex quadratic programming problems in sphere-structured SVMs is small, but also the number of variables in each programming is least. The computing time of classification is reduced. Otherwise, the characteristics of sphere-structured SVMs make expand data easily.