A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Nearest hyperdisk methods for high-dimensional classification
Proceedings of the 25th international conference on Machine learning
A new maximal-margin spherical-structured multi-class support vector machine
Applied Intelligence
Active learning for network intrusion detection
Proceedings of the 2nd ACM workshop on Security and artificial 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
Margin maximization in spherical separation
Computational Optimization and Applications
Hyperdisk based large margin classifier
Pattern Recognition
Toward supervised anomaly detection
Journal of Artificial Intelligence Research
Parallel support vector data description
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Hi-index | 0.00 |
Previous sphere-based classification algorithms usually need a number of spheres in order to achieve good classification performance. In this paper, inspired by the support vector machines for classification and the support vector data description method, we present a new method for constructing single spheres that separate data with the maximum separation ratio. In contrast to previous methods that construct spheres in the input space, the new method constructs separating spheres in the feature space induced by the kernel. As a consequence, the new method is able to construct a single sphere in the feature space to separate patterns that would otherwise be inseparable when using a sphere in the input space. In addition, by adjusting the ratio of the radius of the sphere to the separation margin, it can provide a series of solutions ranging from spherical to linear decision boundaries, effectively encompassing both the support vector machines for classification and the support vector data description method. Experimental results show that the new method performs well on both artificial and real-world datasets.