Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (The Handbooks of Fuzzy Sets)
Convex large margin training techniques: unsupervised, semi-supervised, and robust support vector machines
Efficient multiclass maximum margin clustering
Proceedings of the 25th international conference on Machine learning
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Maximum margin clustering made practical
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
When studying a metastable dynamical system, a prime concern is how to decompose the state space into a set of metastable states. However, the metastable state decomposition based on simulation or experimental data is still a challenge. The most popular and simplest approach is geometric clustering, which was developed based on the classical clustering technique but only works for simple diffusion processes. Recently, the kinetic clustering approach based on state space discretization and transition probability estimation has attracted many attentions for it is applicable to more general systems, but the choice of discretization policy is a difficult task. In this paper, a new decomposition method, called maximum margin metastable clustering, is proposed, which converts the problem of metastable state decomposition into a unsupervised learning problem use the large margin technique to search for the optimal decomposition without state space discretization. Some simulation examples illustrate the effectiveness of the proposed method.