The nature of statistical learning theory
The nature of statistical learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems
The Journal of Machine Learning Research
Nonsmooth Optimization Techniques for Semisupervised Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We consider the problem of multiclass classification where both a few labeled data and lots of unlabeled data are given, for which a new approach called Multi-Agent-System-Based Multi-Class Transductive Learning (MMT) is presented. In MMT, we transform the data classification into a self-organizing Markov stochastic process that finally converges to a stationary probability distribution, in which an optimal label distribution is provided. Based on the proposed approach, an algorithm called Multi-Agent-System-Based Multi-Class Transductive Algorithm (MMTA) was designed and its converging capabilities were discussed. The simulations have shown the effectiveness and practicability of MMTA.